Econometrics and economic statistics Books

Book SynopsisTable of ContentsPreface 1 Introduction 1.1 About Econometrics 1.2 The Structure of This Book 1.3 Illustrations and Exercises 2 An Introduction to Linear Regression 2.1 Ordinary Least Squares as an Algebraic Tool 2.2 The Linear Regression Model 2.3 Small Sample Properties of the OLS Estimator 2.4 Goodness-of-fit 2.5 Hypothesis Testing 2.6 Asymptotic Properties of the OLS Estimator 2.7 Illustration: The Capital Asset Pricing Model 2.8 Multicollinearity 2.9 Missing Data, Outliers and Influential Observations 2.10 Prediction Wrap-up Exercises 3 Interpreting and Comparing Regression Models 3.1 Interpreting the Linear Model 3.2 Selecting the Set of Regressors 3.3 Misspecifying the Functional Form 3.4 Illustration: Explaining House Prices 3.5 Illustration: Predicting Stock Index Returns 3.6 Illustration: Explaining Individual Wages Wrap-up Exercises 4 Heteroskedasticity and Autocorrelation 4.1 Consequences for the OLS Estimator 4.2 Deriving an Alternative Estimator 4.3 Heteroskedasticity 4.4 Testing for Heteroskedasticity 4.5 Illustration: Explaining Labour Demand 4.6 Autocorrelation 4.7 Testing for First-order Autocorrelation 4.8 Illustration: The Demand for Ice Cream 4.9 Alternative Autocorrelation Patterns 4.10 What to do When you Find Autocorrelation? 4.11 Illustration: Risk Premia in Foreign Exchange Markets Wrap-up Exercises 5 Endogenous Regressors, Instrumental Variables and GMM 5.1 A Review of the Properties of the OLS Estimator 5.2 Cases Where the OLS Estimator Cannot be Saved 5.3 The Instrumental Variables Estimator 5.4 Illustration: Estimating the Returns to Schooling 5.5 Alternative Approaches to Estimate Causal Effects 5.6 The Generalized Instrumental Variables Estimator 5.7 Institutions and Economic Development 5.8 The Generalized Method of Moments 5.9 Illustration: Estimating Intertemporal Asset Pricing Models Wrap-up Exercises 6 Maximum Likelihood Estimation and Specification Tests 6.1 An Introduction to Maximum Likelihood 6.2 Specification Tests 6.3 Tests in the Normal Linear Regression Model 6.4 Quasi-maximum Likelihood and Moment Conditions Tests Wrap-up Exercises 7 Models with Limited Dependent Variables 7.1 Binary Choice Models 7.2 Multiresponse Models 7.3 Models for Count Data 7.4 Tobit Models 7.5 Extensions of Tobit Models 7.6 Sample Selection Bias 7.7 Estimating Treatment Effects 7.7.1 Regression-based Estimators 7.8 Duration Models Wrap-up Exercises 8 Univariate Time Series Models 8.1 Introduction 8.2 General ARMA Processes 8.3 Stationarity and Unit Roots 8.4 Testing for Unit Roots 8.5 Illustration: Long-run Purchasing Power Parity (Part 1) 8.6 Estimation of ARMA Models 8.7 Choosing a Model 8.8 Illustration: The Persistence of Inflation 8.9 Forecasting with ARMA Models 8.10 Illustration: The Expectations Theory of the Term Structure 8.11 Autoregressive Conditional Heteroskedasticity 8.12 What about Multivariate Models? Wrap-up Exercises 9 Multivariate Time Series Models 9.1 Dynamic Models with Stationary Variables 9.2 Models with Nonstationary Variables 9.3 Illustration: Long-run Purchasing Power Parity (Part 2) 9.4 Vector Autoregressive Models 9.5 Cointegration: the Multivariate Case 9.6 Illustration: Money Demand and Inflation Wrap-up Exercises 10 Models Based on Panel Data 10.1 Introduction to Panel Data Modelling 10.2 The Static Linear Model 10.3 Illustration: Explaining Individual Wages 10.4 Dynamic Linear Models 10.5 Illustration: Explaining Capital Structure 10.6 Panel Time Series 10.7 Models with Limited Dependent Variables 10.8 Incomplete Panels and Selection Bias 10.9 Pseudo Panels and Repeated Cross-sections Wrap-up A Vectors and Matrices A.1 Terminology A.2 Matrix Manipulations A.3 Properties of Matrices and Vectors A.4 Inverse Matrices A.5 Idempotent Matrices A.6 Eigenvalues and Eigenvectors A.7 Differentiation A.8 Some Least Squares Manipulations B Statistical and Distribution Theory B.1 Discrete Random Variables B.2 Continuous Random Variables B.3 Expectations and Moments B.4 Multivariate Distributions B.5 Conditional Distributions B.6 The Normal Distribution B.7 Related Distributions Bibliograph Index

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Book SynopsisTable of ContentsGetting Started Lesson 1 An Introduction to Adobe Xd CC Lesson 2 Setting Up a Project Lesson 3 Creating Graphics Lesson 4 Adding Images and Text Lesson 5 Organizing Content Lesson 6 Working with Assets And Cc Libraries Lesson 7 Using Effects and Repeat Grids Lesson 8 Prototyping Lesson 9 Sharing Your Prototype Lesson 10 Sharing Design Specs and Exporting

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Book SynopsisThe Sunday Times Bestseller''Tim Harford is one of my favourite writers in the world. His storytelling is gripping but never overdone, his intellectual honesty is rare and inspiring, and his ability to make complex things simple - but not simplistic - is exceptional. How to Make the World Add Up is another one of his gems. If you''re looking for an addictive pageturner that will make you smarter, this is your book'' Rutger Bregman, author of Humankind''Tim Harford could well be Britain''s Malcolm Gladwell''Alex Bellos, author of Alex''s Adventures in Numberland''If you aren''t in love with stats before reading this book, you will be by the time you''re done. Powerful, persuasive, and in these truth-defying times, indispensable''Caroline Criado Perez, author of Invisible Women In How to Make the World Add Up, Tim Harford draws on his experience as both an economTrade ReviewNobody makes the statistics of everyday life more fascinating and enjoyable than Tim Harford -- Bill BrysonHow often do you read a blurb that says 'this book is so timely' or 'now more than ever we need a book like this'? But I promise you, by all that I hold sacred, this has never been truer of any book than it is of HOW TO MAKE THE WORLD ADD UP. We are supremely lucky to have the fabulously readable, lucid, witty and authoritative Tim Harford to remind us why facts, reason, numbers, clarity and truth matter, how beautiful they are and how crucial to our understanding of the natural world and human society. Without the kind of purity and honesty of approach that he stands for the world is doomed. Every politician and journalist should be made to read this book, but everyone else will get so much pleasure and draw so much strength from the joyful way it dispels the clouds of deceit and delusion -- Stephen FryHe's a genius at telling stories that illuminate our world -- Malcolm GladwellAn immensely enjoyable guide to using statistics wisely. I loved it -- Matt Parker, author of HUMBLE PIIf you aren't in love with stats before reading this book, you will be by the time you're done. Powerful, persuasive, and in these truth-defying times, indispensable -- Caroline Criado Perez, author of INVISIBLE WOMENWe live in a world that is awash with statistics, but what should we do when someone makes a claim that they say is based on data? This wise book, distilled from years of experience, gives us the ten commandments, from first examining our feelings, to finally having the humility to admit we may be wrong. Priceless -- Professor Sir David SpiegelhalterTim Harford is one of the finest writers of nonfiction. This is another brilliant read: wise, humane and, above all, illuminating. Nobody is better on statistics and numbers - and how to make sense of them -- Matthew Syed, author of REBEL IDEASFew people write about social science with the clarity and wit of Tim Harford. If you're staggered by statistics or daunted by data, this entertaining romp of a book is essential reading -- Daniel H. Pink * author of WHEN and DRIVE *Thanks to Tim Harford's characteristic wit and magnetic storytelling, you may not realise you're getting an advanced course in how to understand the kind of statistics we're all faced with everyday. HOW TO MAKE THE WORLD ADD UP is certainly a fun book to read, but it's also a genuinely important one -- David Epstein * author of RANGE *In a world where we are worried about misinformation, Harford gives us a brilliant guide which teaches us how to be sceptical without being cynical, and to see that statistics are not scary, but a rare treasure that help us understand our society -- Professor Hetan Shah, chief executive of the British AcademyWise and useful ... such a delight * Financial Times *Tim Harford is one of my favourite writers in the world. His storytelling is gripping but never overdone, his intellectual honesty is rare and inspiring, and his ability to make complex things simple - but not simplistic - is exceptional. How to Make the World Add Up is another one of his gems. If you're looking for an addictive pageturner that will make you smarter, this is your book -- Rutger Bregman, author of Humankind

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Book SynopsisTable of Contents1. Statistics, Data, and Statistical Thinking 2. Methods for Describing Sets of Data 3. Probability 4. Random Variables and Probability Distributions 5. Sampling Distributions 6. Inferences Based on a Single Sample: Estimation with ConfidenceIntervals 7. Inferences Based on a Single Sample: Tests of Hypotheses 8. Inferences Based on Two Samples: Confidence Intervals and Testsof Hypotheses 9. Design of Experiments and Analysis of Variance 10. Categorical Data Analysis 11. Simple Linear Regression 12. Multiple Regression and Model Building 13. Methods for Quality Improvement: Statistical Process Control(Available Online) 14. Time Series: Descriptive Analyses, Models, and Forecasting(Available Online) 15. Nonparametric Statistics (Available Online) Appendix A: Summation Notation Appendix B: Basic Counting Rules Appendix C: Calculation Formulas for Analysis of Variance C.1 Formulas for the Calculationsin the Completely Randomized Design C.2 Formulas for the Calculationsin the Randomized Block Design C.3 Formulas for the Calculationsfor a Two-Factor Factorial Experiment C.4 Tukey's Multiple ComparisonsProcedure (Equal Sample Sizes) C.5 Bonferroni MultipleComparisons Procedure (Pairwise Comparisons) C.6 Scheffé's MultipleComparisons Procedure (Pairwise Comparisons) Appendix D: Tables Answers to Selected Exercises Index Credits

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Book SynopsisIntroducing first year PhD students to standard graduate econometrics material, this work covers the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. It is useful for those who intend to write a thesis on applied topics and also for the theoretically inclined.Trade Review"Students of econometrics and their teachers will find this book to be the best introduction to the subject at the graduate and advanced undergraduate level. Starting with least squares regression, Hayashi provides an elegant exposition of all the standard topics of econometrics, including a detailed discussion of stationary and non-stationary time series. The particular strength of the book is the excellent balance between econometric theory and its applications, using GMM as an organizing principle throughout. Each chapter includes a detailed empirical example taken from classic and current applications of econometrics."—Dale Jorgensen, Harvard University"Econometrics will be a very useful book for intermediate and advanced graduate courses. It covers the topics with an easy to understand approach while at the same time offering a rigorous analysis. The computer programming tips and problems should also be useful to students. I highly recommend this book for an up-to-date coverage and thoughtful discussion of topics in the methodology and application of econometrics."—Jerry A. Hausman, Massachusetts Institute of Technology"Econometrics covers both modern and classic topics without shifting gears. The coverage is quite advanced yet the presentation is simple. Hayashi brings students to the frontier of applied econometric practice through a careful and efficient discussion of modern economic theory. The empirical exercises are very useful. . . . The projects are carefully crafted and have been thoroughly debugged."—Mark W. Watson, Princeton University"Econometrics strikes a good balance between technical rigor and clear exposition. . . . The use of empirical examples is well done throughout. I very much like the use of old 'classic' examples. It gives students a sense of history—and shows that great empirical econometrics is a matter of having important ideas and good data, not just fancy new methods. . . . The style is just great, informal and engaging."—James H. Stock, John F. Kennedy School of Government, Harvard UniversityTable of ContentsList of Figures xvii Preface xix 1 Finite-Sample Properties of OLS 3 1.1 The Classical Linear Regression Model 3 The Linearity Assumption 4 Matrix Notation 6 The Strict Exogeneity Assumption 7 Implications of Strict Exogeneity 8 Strict Exogeneity in Time-Series Models 9 Other Assumptions of the Model 10 The Classical Regression Model for Random Samples 12 "Fixed" Regressors 13 1.2 The Algebra of Least Squares 15 OLS Minimizes the Sum of Squared Residuals 15 Normal Equations 16 Two Expressions for the OLS Estimator 18 More Concepts and Algebra 18 Influential Analysis (optional) 21 A Note on the Computation of OLS Estimates 23 1.3 Finite-Sample Properties of OLS 27 Finite-Sample Distribution of b 27 Finite-Sample Properties of s2 30 Estimate of Var(b | X) 31 1.4 Hypothesis Testing under Normality 33 Normally Distributed Error Terms 33 Testing Hypotheses about Individual Regression Coefficients 35 Decision Rule for the t-Test 37 Confidence Interval 38 p-Value 38 Linear Hypotheses 39 The F-Test 40 A More Convenient Expression for F 42 t versus F 43 An Example of a Test Statistic Whose Distribution Depends on X 45 1.5 Relation to Maximum Likelihood 47 The Maximum Likelihood Principle 47 Conditional versus Unconditional Likelihood 47 The Log Likelihood for the Regression Model 48 ML via Concentrated Likelihood 48 Cramer-Rao Bound for the Classical Regression Model 49 The F-Test as a Likelihood Ratio Test 52 Quasi-Maximum Likelihood 53 1.6 Generalized Least Squares (GLS) 54 Consequence of Relaxing Assumption 1.4 55 Efficient Estimation with Known V 55 A Special Case: Weighted Least Squares (WLS) 58 Limiting Nature of GLS 58 1.7 Application: Returns to Scale in Electricity Supply 60 The Electricity Supply Industry 60 The Data 60 Why Do We Need Econometrics? 61 The Cobb-Douglas Technology 62 How Do We Know Things Are Cobb-Douglas? 63 Are the OLS Assumptions Satisfied? 64 Restricted Least Squares 65 Testing the Homogeneity of the Cost Function 65 Detour: A Cautionary Note on R2 67 Testing Constant Returns to Scale 67 Importance of Plotting Residuals 68 Subsequent Developments 68 Problem Set 71 Answers to Selected Questions 84 2 Large-Sample Theory 88 2.1 Review of Limit Theorems for Sequences of Random Variables 88 Various Modes of Convergence 89 Three Useful Results 92 Viewing Estimators as Sequences of Random Variables 94 Laws of Large Numbers and Central Limit Theorems 95 2.2 Fundamental Concepts in Time-Series Analysis 97 Need for Ergodic Stationarity 97 Various Classes of Stochastic Processes 98 Different Formulation of Lack of Serial Dependence 106 The CLT for Ergodic Stationary Martingale Differences Sequences 106 2.3 Large-Sample Distribution of the OLS Estimator 109 The Model 109 Asymptotic Distribution of the OLS Estimator 113 s2 Is Consistent 115 2.4 Hypothesis Testing 117 Testing Linear Hypotheses 117 The Test Is Consistent 119 Asymptotic Power 120 Testing Nonlinear Hypotheses 121 2.5 Estimating E([not displayable]) Consistently 123 Using Residuals for the Errors 123 Data Matrix Representation of S 125 Finite-Sample Considerations 125 2.6 Implications of Conditional Homoskedasticity 126 Conditional versus Unconditional Homoskedasticity 126 Reduction to Finite-Sample Formulas 127 Large-Sample Distribution of t and F Statistics 128 Variations of Asymptotic Tests under Conditional Homoskedasticity 129 2.7 Testing Conditional Homoskedasticity 131 2.8 Estimation with Parameterized Conditional Heteroskedasticity (optional) 133 The Functional Form 133 WLS with Known [alpha] 134 Regression of e2i on zi Provides a Consistent Estimate of [alpha] 135 WLS with Estimated [alpha] 136 OLS versus WLS 137 2.9 Least Squares Projection 137 Optimally Predicting the Value of the Dependent Variable 138 Best Linear Predictor 139 OLS Consistently Estimates the Projection Coefficients 140 2.10 Testing for Serial Correlation 141 Box-Pierce and Ljung-Box 142 Sample Autocorrelations Calculated from Residuals 144 Testing with Predetermined, but Not Strictly Exogenous, Regressors 146 An Auxiliary Regression-Based Test 147 2.11 Application: Rational Expectations Econometrics 150 The Efficient Market Hypotheses 150 Testable Implications 152 Testing for Serial Correlation 153 Is the Nominal Interest Rate the Optimal Predictor? 156 Rt Is Not Strictly Exogenous 158 Subsequent Developments 159 2.12 Time Regressions 160 The Asymptotic Distribution of the OLS Estimates 161 Hypothesis Testing for Time Regressions 163 2.A Asymptotics with Fixed Regressors 164 2.B Proof of Proposition 2.10 165 Problem Set 168 Answers to Selected Questions 183 3 Single-Equation GMM 186 3.1 Endogeneity Bias: Working's Example 187 A Simultaneous Equations Model of Market Equilibrium 187 Endogeneity Bias 188 Observable Supply Shifters 189 3.2 More Examples 193 A Simple Macroeconometric Model 193 Errors-in-Variables 194 Production Function 196 3.3 The General Formulation 198 Regressors and Instruments 198 Identification 200 Order Condition for Identification 202 The Assumption for Asymptotic Normality 202 3.4 Generalized Method of Moments Defined 204 Method of Moments 205 Generalized Method of Moments 206 Sampling Error 207 3.5 Large-Sample Properties of GMM 208 Asymptotic Distribution of the GMM Estimator 209 Estimation of Error Variance 210 Hypothesis Testing 211 Estimation of S 212 Efficient GMM Estimator 212 Asymptotic Power 214 Small-Sample Properties 215 3.6 Testing Overidentifying Restrictions 217 Testing Subsets of Orthogonality Conditions 218 3.7 Hypothesis Testing by the Likelihood-Ratio Principle 222 The LR Statistic for the Regression Model 223 Variable Addition Test (optional) 224 3.8 Implications of Conditional Homoskedasticity 225 Efficient GMM Becomes 2SLS 226 J Becomes Sargan's Statistic 227 Small-Sample Properties of 2SLS 229 Alternative Derivations of 2SLS 229 When Regressors Are Predetermined 231 Testing a Subset of Orthogonality Conditions 232 Testing Conditional Homoskedasticity 234 Testing for Serial Correlation 234 3.9 Application: Returns from Schooling 236 The NLS-Y Data 236 The Semi-Log Wage Equation 237 Omitted Variable Bias 238 IQ as the Measure of Ability 239 Errors-in-Variables 239 2SLS to Correct for the Bias 242 Subsequent Developments 243 Problem Set 244 Answers to Selected Questions 254 4 Multiple-Equation GMM 258 4.1 The Multiple-Equation Model 259 Linearity 259 Stationarity and Ergodicity 260 Orthogonality Conditions 261 Identification 262 The Assumption for Asymptotic Normality 264 Connection to the "Complete" System of Simultaneous Equations 265 4.2 Multiple-Equation GMM Defined 265 4.3 Large-Sample Theory 268 4.4 Single-Equation versus Multiple-Equation Estimation 271 When Are They "Equivalent"? 272 Joint Estimation Can Be Hazardous 273 4.5 Special Cases of Multiple-Equation GMM: FIVE, 3SLS, and SUR 274 Conditional Homoskedasticity 274 Full-Information Instrumental Variables Efficient (FIVE) 275 Three-Stage Least Squares (3SLS) 276 Seemingly Unrelated Regressions (SUR) 279 SUR versus OLS 281 4.6 Common Coefficients 286 The Model with Common Coefficients 286 The GMM Estimator 287 Imposing Conditional Homoskedasticity 288 Pooled OLS 290 Beautifying the Formulas 292 The Restriction That Isn't 293 4.7 Application: Interrelated Factor Demands 296 The Translog Cost Function 296 Factor Shares 297 Substitution Elasticities 298 Properties of Cost Functions 299 Stochastic Specifications 300 The Nature of Restrictions 301 Multivariate Regression Subject to Cross-Equation Restrictions 302 Which Equation to Delete? 304 Results 305 Problem Set 308 Answers to Selected Questions 320 5 Panel Data 323 5.1 The Error-Components Model 324 Error Components 324 Group Means 327 A Reparameterization 327 5.2 The Fixed-Effects Estimator 330 The Formula 330 Large-Sample Properties 331 Digression: When [eta]i Is Spherical 333 Random Effects versus Fixed Effects 334 Relaxing Conditional Homoskedasticity 335 5.3 Unbalanced Panels (optional) 337 "Zeroing Out" Missing Observations 338 Zeroing Out versus Compression 339 No Selectivity Bias 340 5.4 Application: International Differences in Growth Rates 342 Derivation of the Estimation Equation 342 Appending the Error Term 343 Treatment of [alpha]i 344 Consistent Estimation of Speed of Convergence 345 Appendix 5.A: Distribution of Hausman Statistic 346 Problem Set 349 Answers to Selected Questions 363 6 Serial Correlation 365 6.1 Modeling Serial Correlation: Linear Processes 365 MA(q) 366 MA([infinity]) as a Mean Square Limit 366 Filters 369 Inverting Lag Polynomials 372 6.2 ARMA Processes 375 AR(1) and Its MA([infinity]) Representation 376 Autocovariances of AR(1) 378 AR(p) and Its MA([infinity]) Representation 378 ARMA(p,q) 380 ARMA(p) with Common Roots 382 Invertibility 383 Autocovariance-Generating Function and the Spectrum 383 6.3 Vector Processes 387 6.4 Estimating Autoregressions 392 Estimation of AR(1) 392 Estimation of AR(p) 393 Choice of Lag Length 394 Estimation of VARs 397 Estimation of ARMA(p,q) 398 6.5 Asymptotics for Sample Means of Serially Correlated Processes 400 LLN for Covariance-Stationary Processes 401 Two Central Limit Theorems 402 Multivariate Extension 404 6.6 Incorporating Serial Correlation in GMM 406 The Model and Asymptotic Results 406 Estimating S When Autocovariances Vanish after Finite Lags 407 Using Kernels to Estimate S 408 VARHAC 410 6.7 Estimation under Conditional Homoskedasticity (Optional) 413 Kernel-Based Estimation of S under Conditional Homoskedasticity 413 Data Matrix Representation of Estimated Long-Run Variance 414 Relation to GLS 415 6.8 Application: Forward Exchange Rates as Optimal Predictors 418 The Market Efficiency Hypothesis 419 Testing Whether the Unconditional Mean Is Zero 420 Regression Tests 423 Problem Set 428 Answers to Selected Questions 441 7 Extremum Estimators 445 7.1 Extremum Estimators 446 "Measurability" of [theta] 446 Two Classes of Extremum Estimators 447 Maximum Likelihood (ML) 448 Conditional Maximum Likelihood 450 Invariance of ML 452 Nonlinear Least Squares (NLS) 453 Linear and Nonlinear GMM 454 7.2 Consistency 456 Two Consistency Theorems for Extremum Estimators 456 Consistency of M-Estimators 458 Concavity after Reparameterization 461 Identification in NLS and ML 462 Consistency of GMM 467 7.3 Asymptotic Normality 469 Asymptotic Normality of M-Estimators 470 Consistent Asymptotic Variance Estimation 473 Asymptotic Normality of Conditional ML 474 Two Examples 476 Asymptotic Normality of GMM 478 GMM versus ML 481 Expressing the Sampling Error in a Common Format 483 7.4 Hypothesis Testing 487 The Null Hypothesis 487 The Working Assumptions 489 The Wald Statistic 489 The Lagrange Multiplier (LM) Statistic 491 The Likelihood Ratio (LR) Statistic 493 Summary of the Trinity 494 7.5 Numerical Optimization 497 Newton-Raphson 497 Gauss-Newton 498 Writing Newton-Raphson and Gauss-Newton in a Common Format 498 Equations Nonlinear in Parameters Only 499 Problem Set 501 Answers to Selected Questions 505 8 Examples of Maximum Likelihood 507 8.1 Qualitative Response (QR) Models 507 Score and Hessian for Observation t 508 Consistency 509 Asymptotic Normality 510 8.2 Truncated Regression Models 511 The Model 511 Truncated Distributions 512 The Likelihood Function 513 Reparameterizing the Likelihood Function 514 Verifying Consistency and Asymptotic Normality 515 Recovering Original Parameters 517 8.3 Censored Regression (Tobit) Models 518 Tobit Likelihood Function 518 Reparameterization 519 8.4 Multivariate Regressions 521 The Multivariate Regression Model Restated 522 The Likelihood Function 523 Maximizing the Likelihood Function 524 Consistency and Asymptotic Normality 525 8.5 FIML 526 The Multiple-Equation Model with Common Instruments Restated 526 The Complete System of Simultaneous Equations 529 Relationship between ([Gamma]0, [Beta]0) and [delta]0 530 The FIML Likelihood Function 531 The FIML Concentrated Likelihood Function 532 Testing Overidentifying Restrictions 533 Properties of the FIML Estimator 533 ML Estimation of the SUR Model 535 8.6 LIML 538 LIML Defined 538 Computation of LIML 540 LIML versus 2SLS 542 8.7 Serially Correlated Observations 543 Two Questions 543 Unconditional ML for Dependent Observations 545 ML Estimation of AR.1/ Processes 546 Conditional ML Estimation of AR(1) Processes 547 Conditional ML Estimation of AR(p) and VAR(p) Processes 549 Problem Set 551 9 Unit-Root Econometrics 557 9.1 Modeling Trends 557 Integrated Processes 558 Why Is It Important to Know if the Process Is I(1)? 560 Which Should Be Taken as the Null, I(0) or I(1)? 562 Other Approaches to Modeling Trends 563 9.2 Tools for Unit-Root Econometrics 563 Linear I(0) Processes 563 Approximating I(1) by a Random Walk 564 Relation to ARMA Models 566 The Wiener Process 567 A Useful Lemma 570 9.3 Dickey-Fuller Tests 573 The AR(1) Model 573 Deriving the Limiting Distribution under the I(1) Null 574 Incorporating the Intercept 577 Incorporating Time Trend 581 9.4 Augmented Dickey-Fuller Tests 585 The Augmented Autoregression 585 Limiting Distribution of the OLS Estimator 586 Deriving Test Statistics 590 Testing Hypotheses about [zeta] 591 What to Do When p Is Unknown? 592 A Suggestion for the Choice of pmax(T) 594 Including the Intercept in the Regression 595 Incorporating Time Trend 597 Summary of the DF and ADF Tests and Other Unit-Root Tests 599 9.5 Which Unit-Root Test to Use? 601 Local-to-Unity Asymptotics 602 Small-Sample Properties 602 9.6 Application: Purchasing Power Parity 603 The Embarrassing Resiliency of the Random Walk Model? 604 Problem Set 605 Answers to Selected Questions 619 10 Cointegration 623 10.1 Cointegrated Systems 624 Linear Vector I(0) and I(1) Processes 624 The Beveridge-Nelson Decomposition 627 Cointegration Defined 629 10.2 Alternative Representations of Cointegrated Systems 633 Phillips's Triangular Representation 633 VAR and Cointegration 636 The Vector Error-Correction Model (VECM) 638 Johansen's ML Procedure 640 10.3 Testing the Null of No Cointegration 643 Spurious Regressions 643 The Residual-Based Test for Cointegration 644 Testing the Null of Cointegration 649 10.4 Inference on Cointegrating Vectors 650 The SOLS Estimator 650 The Bivariate Example 652 Continuing with the Bivariate Example 653 Allowing for Serial Correlation 654 General Case 657 Other Estimators and Finite-Sample Properties 658 10.5 Application: the Demand for Money in the United States 659 The Data 660 (m - p, y, R) as a Cointegrated System 660 DOLS 662 Unstable Money Demand? 663 Problem Set 665 Appendix. Partitioned Matrices and Kronecker Products 670 Addition and Multiplication of Partitioned Matrices 671 Inverting Partitioned Matrices 672

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Book SynopsisOriginally published in 1930, this book provides a concise discussion of the relationship between theoretical statistics and actuarial science. This book will be of value to anyone with an interest in the actuarial profession, statistics and the history of finance.Table of ContentsPreface; 1. Introductory observations on the place of mathematics in statistical and actuarial theory; 2. A mathematical investigation of the limits of error in the calculation of āx, when the force of mortality follows Makeham's Law; 3. The theoretical foundation of various types of frequency functions; Notation; Index.

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Book SynopsisShows how the basic tools of applied econometrics allow the data to speak. This book covers regression-discontinuity designs and quantile regression - as well as how to get standard errors right. It is suitable for various areas in contemporary social science.Trade Review"A quirky and thought-provoking read for any budding econometrician... Insightful and refreshing."--James Davidson, Times Higher Education "I'd recommend it to the entire range of empirical economists, from those still in training to those who, like me, have only a hazy memory of statistical theory and stick to our tried and tested methods of estimation ... an excellent guide to how to do basic regression/IV/panel data estimation really well. In particular, it demonstrates through many examples how to bring about a happy marriage between one's underlying model and the data which might or might not confirm the researcher's hypotheses."--Diane Coyle, The Enlightened Economist Blog "The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social sciences."--Pavel Stoynov, Zentralblatt MATH "[T]he matter covered in the book is surely of interest to most agricultural economists. Even if it is not a complete overview of existing econometric research methods, it certainly contains a good deal of hands on advice driven by years of experience."--European Review of Agricultural Economics "This book is an extremely thought-provoking contribution to the literature. It champions a different paradigm to that characterising most econometrics texts and does so with considerable (idiosyncratic) style and grace. Highly recommended!"--David Harris and Christopher L. Skeels, Economic RecordTable of ContentsList of Figures vii List of Tables ix Preface xi Acknowledgments xv Organization of This Book xvii PART I: PRELIMINARIES 1 Chapter 1: Questions about Questions 3 Chapter 2: The Experimental Ideal 11 2.1 The Selection Problem 12 2.2 Random Assignment Solves the Selection Problem 15 2.3 Regression Analysis of Experiments 22 PART II: THE CORE 25 Chapter 3: Making Regression Make Sense 27 3.1 Regression Fundamentals 28 3.2 Regression and Causality 51 3.3 Heterogeneity and Nonlinearity 68 3.4 Regression Details 91 3.5 Appendix: Derivation of the Average Derivative Weighting Function 110 Chapter 4: Instrumental Variables in Action: Sometimes You Get What You Need 113 4.1 IV and Causality 115 4.2 Asymptotic 2SLS Inference 138 4.3 Two-Sample IV and Split-Sample IV 147 4.4 IV with Heterogeneous Potential Outcomes 150 4.5 Generalizing LATE 173 4.6 IV Details 188 4.7 Appendix 216 Chapter 5: Parallel Worlds: Fixed Effects, Differences-in-Differences, and Panel Data 221 5.1 Individual Fixed Effects 221 5.2 Differences-in-Differences 227 5.3 Fixed Effects versus Lagged Dependent Variables 243 5.4 Appendix: More on Fixed Effects and Lagged Dependent Variables 246 PART III: EXTENSIONS 249 Chapter 6: Getting a Little Jumpy: Regression Discontinuity Designs 251 6.1 Sharp RD 251 6.2 Fuzzy RD Is IV 259 Chapter 7: Quantile Regression 269 7.1 The Quantile Regression Model 270 7.2 IV Estimation of Quantile Treatment Effects 283 Chapter 8: Nonstandard Standard Error Issues 293 8.1 The Bias of Robust Standard Error Estimates 294 8.2 Clustering and Serial Correlation in Panels 308 8.3 Appendix: Derivation of the Simple Moulton Factor 323 Last Words 327 Acronyms and Abbreviations 329 Empirical Studies Index 335 References 339 Index 361

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Book SynopsisApplied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu-themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstratTrade Review"I would be hard pressed to name another econometrics book that can be read for enjoyment yet provides useful quantitative insights."--M.S.R., Financial Analysts JournalTable of ContentsList of Figures vii List of Tables ix Introduction xi 1 Randomized Trials 1 1.1 In Sickness and in Health (Insurance) 1 1.2 The Oregon Trail 24 Masters of 'Metrics: From Daniel to R. A. Fisher 30 Appendix: Mastering Inference 33 2 Regression 47 2.1 A Tale of Two Colleges 47 2.2 Make Me a Match, Run Me a Regression 55 2.3 Ceteris Paribus? 68 Masters of 'Metrics: Galton and Yule 79 Appendix: Regression Theory 82 3 Instrumental Variables 98 3.1 The Charter Conundrum 99 3.2 Abuse Busters 115 3.3 The Population Bomb 123 Masters of 'Metrics: The Remarkable Wrights 139 Appendix: IV Theory 142 4 Regression Discontinuity Designs 147 4.1 Birthdays and Funerals 148 4.2 The Elite Illusion 164 Masters of 'Metrics: Donald Campbell 175 5 Differences-in-Differences 178 5.1 A Mississippi Experiment 178 5.2 Drink, Drank, ... 191 Masters of 'Metrics: John Snow 204 Appendix: Standard Errors for Regression DD 205 6 The Wages of Schooling 209 6.1 Schooling, Experience, and Earnings 209 6.2 Twins Double the Fun 217 6.3 Econometricians Are Known by Their ... Instruments 223 6.4 Rustling Sheepskin in the Lone Star State 235 Appendix: Bias from Measurement Error 240 Abbreviations and Acronyms 245 Empirical Notes 249 Acknowledgments 269 Index 271

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Book SynopsisDr. Bill Carlson is professor emeritus of economics at St. Olaf College, where he taught for 31 years, serving several times as department chair and in various administrative functions, including director of academic computing. He has also held leave assignments with the U.S. government and the University of Minnesota in addition to lecturing at many different universities. He was elected an honorary member of Phi Beta Kappa. In addition, he spent 10 years in private industry and contract research prior to beginning his career at St. Olaf. His education includes engineering degrees from Michigan Technological University (BS) and from the Illinois Institute of Technology (MS) and a PhD in quantitative management from the Rackham Graduate School at the University of Michigan. Numerous research projects related to management, highway safety, and statistical education have produced more than50 publications. He received the Metropolitan Insurance Award of Merit for SafetTable of Contents Describing Data: Graphical Describing Data: Numerical Probability Discrete Random Variables and Probability Distributions Continuous Random Variables and Probability Distributions Sampling and Sampling Distributions Estimation: Single Population Estimation: Additional Topics Hypothesis Testing: Single Population Hypothesis Testing: Additional Topics Simple Regression Multiple Regression Additional Topics in Regression Analysis Analysis of Categorical Data Analysis of Variance Time-Series Analysis and Forecasting Additional Topics in Sampling

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Book SynopsisUsing a reader-friendly style and straightforward, interesting approach, SUPPLY CHAIN MANAGEMENT: A LOGISTICS PERSPECTIVE, 11E blends logistics theory with practical applications. The latest content highlights emerging issues, technology developments, and global changes in the constantly evolving field of supply chain management today. This digital edition examines today's real companies and how public and private organizations are responding to the continual pressure to modernize and transform their supply chains. Updated features and short cases offer hands-on managerial experience as you examine the key decisions and circumstances that supply chain managers face daily. New profiles introduce each chapter with real organizations, people, or events that emphasize the relevance of what you are learning. Technology-focused features and global content examine key areas where change is occurring and provide a meaningful perspective on how today's changes impact current and future supply cTable of ContentsPart I. Supply Chain Foundations. 1. Supply Chain Management: An Overview. 2. Global Dimensions of Supply Chains. 3. Role of Logistics in Supply Chains. 4. Supply Chain and Omni Channel Network Design. Part II. Supply Chain Fundamentals. 5. Sourcing Materials and Services. 6. Operations ��� Producing Goods and Services. 7. Demand Management. 8. Order Management and Customer Service. Part III. Cross-Chain Logistics Processes. 9. Managing Inventory in the Supply Chain. 10 Distribution ��� Managing Fulfillment Operations. 11. Transportation ��� Managing the Flows of the Supply Chain. Part IV. Supply Chain Challenges and Future Directions. 12. Aligning Supply Chains. 13. Supply Chain Performance Measurement and Financial Analysis. 14. Supply Chain Technology ��� Managing Information Flows. 15. Strategic Challenges and Change for Supply Chains.

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Book SynopsisA provocative new analysis of immigration's long-term effects on a nation's economy and culture. Over the last two decades, as economists began using big datasets and modern computing power to reveal the sources of national prosperity, their statistical results kept pointing toward the power of culture to drive the wealth of nations. In The Culture Transplant, Garett Jones documents the cultural foundations of cross-country income differences, showing that immigrants import cultural attitudes from their homelands—toward saving, toward trust, and toward the role of government—that persist for decades, and likely for centuries, in their new national homes. Full assimilation in a generation or two, Jones reports, is a myth. And the cultural traits migrants bring to their new homes have enduring effects upon a nation's economic potential. Built upon mainstream, well-reviewed academic research that hasn't pierced the public consciousness, this book offers a compelling refutation of an unspoken consensus that a nation's economic and political institutions won't be changed by immigration. Jones refutes the common view that we can discuss migration policy without considering whether migration can, over a few generations, substantially transform the economic and political institutions of a nation. And since most of the world's technological innovations come from just a handful of nations, Jones concludes, the entire world has a stake in whether migration policy will help or hurt the quality of government and thus the quality of scientific breakthroughs in those rare innovation powerhouses. Trade Review"Immigrants change the countries they move to. The Culture Transplant is the very best book on this phenomenon, reflecting the continuing rise of Garett Jones as a thinker and writer of real import."—Tyler Cowen, blogger, Marginal Revolution"Synthesizing decades of new work in development economics, Garett Jones re-examines and rejects some of the core assumptions within the modern immigration debate. Defenders of open borders—utilitarians in particular—will have to seriously grapple with this novel and groundbreaking book."—Hrishikesh Joshi, Bowling Green State University"A unique and authoritative treatment of the deep persistence of cultural attributes that permeates across generations, and through migration, shapes institutions and contemporary outcomes. By focusing on people rather than places, Garett Jones provides a unique perspective on how we should think about the role of migration and diversity in understanding modern successes and failures. Jones's treatment of the literature is a master class in distilling rigorous research and presenting it in a breezy fashion that is hard to put down once you get started."—Areendam Chanda, Louisiana State University"The Culture Transplant is a good read, a brief dive into the intriguing question of why some places and some people are so much more prosperous than others."—Robert VerBruggen, Wall Street JournalMuch of the literature on immigrant assimilation looks at easily observable questions about subsequent generations, such as whether they are learning English, graduating high school, and moving up the income ladder. Jones's book proves that these external accomplishments do not necessarily indicate assimilation at the deeper level of cultural values. This is of the greatest possible importance, because every day social science discovers further evidence that these cultural values, more than anything else, determine what a country's politics and its economy will look like in the future."—Helen Andrews, The American Conservative"Jones has written an excellent synopsis of the deep roots of culture and the persistent effects of these deep roots. The book is concise and easy to read, led by Jones's ability to decompose complicated ideas into easily understood examples and descriptions. Researchers and the public will gain valuable insights from The Cultural Transplant, a better understanding of the persistence of culture and longrunning factors that have placed countries on socioeconomic trajectories that have yielded vast differences in living standards across the world."—C. Justin Cook, The Developing Economies"Most economists agree that immigration—including illegal immigration—leads to greater economic growth and innovation. However, Jones argues that immigrants transplant their culture in the countries they move to, making the economies there similar to those in their home countries. Where immigrants come from and their home nation's technological development are critical to the economic and cultural impacts they have on the countries they move to."—P. Z. McKay, CHOICETable of Contents0. Preface: The Best Immigration Policy 0. Introduction: How Economists Learned the Power of Culture 1. The Assimilation Myth 2. Prosperity Migrates 3. Places or Peoples? 4. The Migration of Good Government 5. Our Diversity Is Our ____________ 6. The I-7 7. The Chinese Diaspora: Building the Capitalist Road 8. The Deep Roots across the Fifty United States 9. Intercalary: Je ne sais quoi 10. Conclusion: The Goose and the Golden Eggs

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Book SynopsisEmpirical Finance: Theory and Application offers a modern, data-driven introduction to the field of finance, tailored for undergraduate students and practitioners seeking to bridge theory with real-world evidence. In an era defined by abundant data and computational power, this book emphasizes hands-on learning by integrating financial theory, empirical analysis, and practical implementation using Python and R. Each chapter balances intuitive explanations with mathematical rigor, ensuring that readers not only understand key concepts but also learn how to test them with actual data.Structured in two parts, the book begins with a thorough review of essential quantitative toolsâoptimization, probability, and statisticsâproviding the foundation needed for empirical work. The second part applies these tools to core topics in finance, including asset pricing, portfolio choice, market efficiency, event studies, and volatility modeling. Real-world examples and case studiesâsuch as testing the Efficient Markets Hypothesis, analyzing stock splits, and evaluating the equity premiumâbring the material to life and illustrate how empirical methods can validate or challenge economic intuition.A distinctive feature of this text is its emphasis on reproducibility and application. Code snippets, exercises, and datasets enable readers to replicate results and develop their own analyses. Topics like time-series properties of returns, portfolio management and behavioral finance are treated with both theoretical and empirical depth, preparing students for quantitative internships, graduate studies, or roles in the financial industry.Ideal for courses in Empirical Finance, Financial Econometrics, or Quantitative Finance, this book stands out for its clear exposition, relevance to contemporary practice, and commitment to evidence-based reasoning. It empowers a new generation of finance students to think critically, work with data, and understand markets not as a set of abstract rules, but as a dynamic interplay of economics, data, and technology.Key Features: Seamlessly integrates hands-on coding in both Python and R with financial theory, enabling readers to replicate results and conduct their own empirical analysis. Strikes a unique balance between financial intuition, mathematical clarity, and real-world application, avoiding the common extremes of abstract theory or mere data manipulation. Structured in two distinct partsâfirst building essential quantitative tools (optimization, probability, statistics) before applying them to core finance topicsâensuring a solid foundation for empirical work. Uses contemporary, relevant examples throughout, such as testing market anomalies, analyzing cryptocurrency returns, and conducting event studies on recent scandals. Emphasizes a data-centric approach to validate or challenge economic reasoning, teaching students to treat finance as a dynamic, evidence-based discipline.

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Book SynopsisThe Second Edition of Advanced Issues in Partial Least Squares Structural Equation Modeling offers a straightforward and practical guide to PLS-SEM for users ready to go further than the basics of A Primer on Partial Least Squares Structural Equation Modeling,Third Edition. Even in this advanced guide, the authors have limited the emphasis on equations, formulas, and Greek symbols, and instead rely on detailed explanations of the fundamentals of PLS-SEM and provide general guidelines for understanding and evaluating the results of applying the method. A single study on corporate reputation features as an example throughout the book, along with a single software package (SmartPLS 4.0) to provide a seamless learning experience. The approach of this book is based on the authors' many years of conducting research and teaching methodology courses, including developing the SmartPLS software. The prTrade Review"Excellent guide on how to use smart pls. Good starter product for understanding the underlying concepts." -- Saurabh Gupta"Must have if you want to do PLS" -- Jason XiongTable of ContentsChapter 1: An Overview of Recent and Emerging Developments in PLS-SEM Chapter 2: Higher-order Constructs Chapter 3: Advanced Modeling and Model Assessment Chapter 4: Advanced Results Illustration Chapter 5: Modeling Observed Heterogeneity Chapter 6: Modeling Unobserved Heterogeneity

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Book Synopsis* Now in 4 colour and accompanied by an outstanding suite of resources. * Combines a non-rigorous approach to mathematics with applications in economics and business.Table of ContentsIntroduction xiii Chapter 1 Mathematical Preliminaries 1 1.1 Some Mathematical Preliminaries 2 1.2 Arithmetic Operations 3 1.3 Fractions 6 1.4 Solving Equations 11 1.5 Currency Conversions 14 1.6 Simple Inequalities 18 1.7 Calculating Percentages 21 1.8 The Calculator. Evaluation and Transposition of Formulae 24 1.9 Introducing Excel 28 Chapter 2 The Straight Line and Applications 37 2.1 The Straight Line 38 2.2 Mathematical Modelling 54 2.3 Applications: Demand, Supply, Cost, Revenue 59 2.4 More Mathematics on the Straight Line 76 2.5 Translations of Linear Functions 82 2.6 Elasticity of Demand, Supply and Income 83 2.7 Budget and Cost Constraints 91 2.8 Excel for Linear Functions 92 2.9 Summary 97 Chapter 3 Simultaneous Equations 101 3.1 Solving Simultaneous Linear Equations 102 3.2 Equilibrium and Break-even 111 3.3 Consumer and Producer Surplus 128 3.4 The National Income Model and the IS-LM Model 133 3.5 Excel for Simultaneous Linear Equations 137 3.6 Summary 142 Appendix 143 Chapter 4 Non-linear Functions and Applications 147 4.1 Quadratic, Cubic and Other Polynomial Functions 148 4.2 Exponential Functions 170 4.3 Logarithmic Functions 184 4.4 Hyperbolic (Rational) Functions of the Form a/(bx + c) 197 4.5 Excel for Non-linear Functions 202 4.6 Summary 205 Chapter 5 Financial Mathematics 209 5.1 Arithmetic and Geometric Sequences and Series 210 5.2 Simple Interest, Compound Interest and Annual Percentage Rates 218 5.3 Depreciation 228 5.4 Net Present Value and Internal Rate of Return 230 5.5 Annuities, Debt Repayments, Sinking Funds 236 5.6 The Relationship between Interest Rates and the Price of Bonds 248 5.7 Excel for Financial Mathematics 251 5.8 Summary 254 Appendix 256 Chapter 6 Differentiation and Applications 259 6.1 Slope of a Curve and Differentiation 260 6.2 Applications of Differentiation, Marginal Functions, Average Functions 270 6.3 Optimisation for Functions of One Variable 286 6.4 Economic Applications of Maximum and Minimum Points 304 6.5 Curvature and Other Applications 320 6.6 Further Differentiation and Applications 334 6.7 Elasticity and the Derivative 347 6.8 Summary 357 Chapter 7 Functions of Several Variables 361 7.1 Partial Differentiation 362 7.2 Applications of Partial Differentiation 380 7.3 Unconstrained Optimisation 400 7.4 Constrained Optimisation and Lagrange Multipliers 410 7.5 Summary 422 Chapter 8 Integration and Applications 427 8.1 Integration as the Reverse of Differentiation 428 8.2 The Power Rule for Integration 429 8.3 Integration of the Natural Exponential Function 435 8.4 Integration by Algebraic Substitution 436 8.5 The Definite Integral and the Area under a Curve 441 8.6 Consumer and Producer Surplus 448 8.7 First-order Differential Equations and Applications 456 8.8 Differential Equations for Limited and Unlimited Growth 468 8.9 Integration by Substitution and Integration by Parts website only 8.10 Summary 474 Chapter 9 Linear Algebra and Applications 477 9.1 Linear Programming 478 9.2 Matrices 488 9.3 Solution of Equations: Elimination Methods 498 9.4 Determinants 504 9.5 The Inverse Matrix and Input/Output Analysis 518 9.6 Excel for Linear Algebra 531 9.7 Summary 534 Chapter 10 Difference Equations 539 10.1 Introduction to Difference Equations 540 10.2 Solution of Difference Equations (First-order) 542 10.3 Applications of Difference Equations (First-order) 554 10.4 Summary 564 Solutions to Progress Exercises 567 Worked Examples 653 Index 659

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Book Synopsis''A statistical national treasure'' Jeremy Vine, BBC Radio 2''Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force'' Popular ScienceDo busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever. In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial. ''Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world'' NatureTrade ReviewDavid Spiegelhalter is probably the greatest living statistical communicator; more than that, he's one of the great communicators in any field. This marvellous book will transform your relationship with the numbers that swirl all around us. Read it and learn. -- Tim HarfordThere is something in here for everyone ... A call to arms for greater societal data literacy ... Spiegelhalter's work serves as a reminder that there are passionate, self-aware statisticians who can argue eloquently that their discipline is needed now more than ever. * Financial Times *Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world . . . The Art of Statistics will serve students well. And it will be a boon for journalists eager to use statistics responsibly - along with anyone who wants to approach research and its reportage with healthy scepticism. * Nature *What David Spiegelhalter does here is provide a very thorough introductory grounding in statistics without making use of mathematical formulae. And it's remarkable. Spiegelhalter is warm and encouraging - it's a genuinely enjoyable read ... This book should be required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force. * Popular Science *The Art of Statistics is in the great educational tradition of its publishing imprint, Pelican Books: an attempt to get everyone up to speed with the practical uses of statistics, without pages of terrifying equations or Greek letters. In a series of spry, airy chapters, he succeeds fabulously ... Lucid and readable. In an age of scientific clickbait, 'big data' and personalised medicine, this is a book that nearly everyone would benefit from reading. * Spectator *Important and comprehensive -- Hannah Fry * New Yorker *This is an excellent book. Spiegelhalter is great at explaining difficult ideas . . . Yes, statistics can be difficult. But much less difficult if you read this book. * Evening Standard *Like the fictional investigator Sherlock Holmes, Spiegelhalter takes readers on a trail to challenge methodology and stats thrown at us by the media and others. But where other authors have attempted this and failed, he is inventive and clever in picking the right examples that spark the reader's interest to become active on their own. * Engineering and Technology *Do you trust headlines telling you . . . that bacon, ham and sausages carry the same cancer risk as cigarettes? No, nor do I. That is why we need a book like this that explains how such implausible nonsense arises in the first place. Written by a master of the subject . . . this book tells us to examine our assumptions. Bravo. * Standpoint *

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Book Synopsis''Refreshingly clear and engaging'' Tim Harford''Delightful . . . full of unique insights'' Prof Sir David SpiegelhalterThere''s no getting away from statistics. We encounter them every day. We are all users of statistics whether we like it or not.Do missed appointments really cost the NHS 1bn per year?What''s the difference between the mean gender pay gap and the median gender pay gap?How can we work out if a claim that we use 42 billion single-use plastic straws per year in the UK is accurate?What did the Vote Leave campaign''s 350m bus really mean?How can we tell if the headline ''Public pensions cost you 4,000 a year'' is correct?Does snow really cost the UK economy 1bn per day?But how do we distinguish statistical fact from fiction? What can we do to decide whether a number, claim or news story is accurate? Without an understanding of data, we cannot truly understand what is going on in the woTrade ReviewFascinating . . . timely . . . a lovely humorous undercurrent to it all -- Marcus Berkmann * Daily Mail *A refreshingly clear and engaging guide to the statistical claims all around us * Tim Harford, author of Fifty Things That Made The Modern Economy & Presenter of BBC More or Less *Having spent his journalistic career working in a newsroom, being inundated with press releases full with dodgy statistics, Reuben has learned all the ways in which numbers can tell a misleading story. In this delightful book, full of unique insights from personal experience, he warns us of the phrases to look out for, and all the questions to ask about shabby surveys and dubious economic forecasts - there's also a great chapter on how to interpret big numbers. And he advises that we all ask the big question - is this number reasonably likely to be true? * Prof Sir David Spiegelhalter *Statistics can clarify or confuse. That's why you need to read this book * John Humphrys *

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Book SynopsisCovers the spectrum of empirical finance, including the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, and the term structure of interest rates, dynamic models of economic equilibrium.Trade ReviewWinner of the 2014 Eugene Fama Prize for Outstanding Contributions to Doctoral Education, University of Chicago Booth School of Business Winner of the 1997 Award for Best Professional/Scholarly Book in Economics, Association of American Publishers Winner of the 1997 Paul A. Samuelson Award, TIAA-CREF "The definitive work explaining this complex but important field of academic endeavor. Oh, and by the way, it's not just academic. The big question that financial econometircs addresses is: What can you learn about the future from the financial data available from the past? This broad issue can be specified in many different ways, and all the important ones are discussed in the book... The vast literature on all the topics examined is assessed, rendered coherent, and then analysed by three men who themselves have made significant advances in the field."--Ruben Lee, London Financial Market "This book is sophisticated, yet accessible; full of details, yet intriguing... Instructors will appreciate the attempt to make each chapter as self contained as possible which leaves them free to choose specified sequences of topics. Professionals will be pleased with the quick and authoritative introductions to important areas of Finance... [A] well written introduction (indeed, something more) to Financial Econometrics. It is alert, explicit and articulate about assumptions... a splendid offering... "--Maurizio Tiso, Review of Financial Studies "Written by the "A" team of financial empiricism, it is a long awaited book. It covers many topics one could only usually find couched in the technical jargon of research papers, presented in this volume with pedagogical intentions. The language, while remaining technical, is quite accessible. It can be effortlessly read by scientific traders with standard knowledge of statistical methods... This book should be made mandatory reading in research departments."--Derivative StrategiesTable of ContentsList of Figures xiii List of Tables xv Preface xix 1Introduction 3 1.1 Organization of the Book 4 1.2 Useful Background 6 1.2.1 Mathematics Background 6 1.2.2 Probability and Statistics Background 6 1.2.3 Finance Theory Background 7 1.3 Notation 8 1.4 Prices, Returns, and Compounding 9 1.4.1 Definitions and Conventions 9 1.4.2 The Marginal, Conditional, and Joint Distribution of Returns 13 1.5 Market Efficiency 20 1.5.1 Efficient Markets and the Law of Iterated Expectations 22 1.5.2 Is Market Efficiency Testable? 24 2The Predictability of Asset Returns 27 2.1 The Random Walk Hypotheses 28 2.1.1 The Random Walk 1: IID Increments 31 2.1.2 The Random Walk 2: Independent Increments 32 2.1.3 The Random Walk 3: Uncorrelated Increments 33 2.2 Tests of Random Walk 1: IID Increments 33 2.2.1 Traditional Statistical Tests 33 2.2.2 Sequences and Reversals, and Runs 34 2.3 Tests of Random Walk 2: Independent Increments 41 2.3.1 Filter Rules 42 2.3.2 Technical Analysis 43 2.4 Tests of Random Walk 3: Uncorrelated Increments 44 2.4.1 Autocorrelation Coefficients 44 2.4.2 Portmanteau Statistics 47 2.4.3 Variance Ratios 48 2.5 Long-Horizon Returns 55 2.5.1 Problems with Long-Horizon Inferences 57 2.6 Tests For Long-Range Dependence 59 2.6.1 Examples of Long-Range Dependence 59 2.6.2 The Hurst-Mandelbrot Rescaled Range Statistic 62 2.7 Unit Root Tests 64 2.8 Recent Empirical Evidence 65 2.8.1 Autocorrelations 66 2.8.2 Variance Ratios 68 2.8.3 Cross-Autocorrelations and Lead-Lag Relations 74 2.8.4 Tests Using Long-Horizon Returns 78 2.9 Conclusion 80 3Market Microstructure 83 3.1 Nonsynchronous Trading 84 3.1.1 A Model of Nonsynchronous Trading 85 3.1.2 Extensions and Generalizations 98 3.2 The Bid-Ask Spread 99 3.2.1 Bid-Ask Bounce 101 3.2.2 Components of the Bid-Ask Spread 103 3.3 Modeling Transactions Data 107 3.3.1 Motivation 108 3.3.2 Rounding and Barrier Models 114 3.3.3 The Ordered Probit Model 122 3.4 Recent Empirical Findings 128 3.4.1 Nonsynchronous Trading 128 3.4.2 Estimating the Effective Bid-Ask Spread 134 3.4.3 Transactions Data 136 3.5 Conclusion 144 4Event-Study Analysis 149 4.1 Outline of an Event Study 150 4.2 An Example of an Event Study 152 4.3 Models for Measuring Normal Performance 153 4.3.1 Constant-Mean-Return Model 154 4.3.2 Market Model 155 4.3.3 Other Statistical Models 155 4.3.4 Economic Models 156 4.4 Measuring and Analyzing Abnormal Returns 157 4.4.1 Estimation of the Market Model 158 4.4.2 Statistical Properties of Abnormal Returns 159 4.4.3 Aggregation of Abnormal Returns 160 4.4.4 Sensitivity to Normal Return Model 162 4.4.5 CARs for the Earnings-Announcement Example 163 4.4.6 Inferences with Clustering 166 4.5 Modifying the Null Hypothesis 167 4.6 Analysis of Power 168 4.7 Nonparametric Tests 172 4.8 Cross-Sectional Models 173 4.9 Further Issues 175 4.9.1 Role of the Sampling Interval 175 4.9.2 Inferences with Event-Date Uncertainty 176 4.9.3 Possible Biases 177 4.10 Conclusion 178 5The Capital Asset Pricing Model 181 5.1 Review of the CAPM 181 5.2 Results from Efficient-Set Mathematics 184 5.3 Statistical Framework for Estimation and Testing 188 5.3.1 Sharpe-Lintner Version 189 5.3.2 Black Version 196 5.4 Size of Tests 203 5.5 Power of Tests 204 5.6 Nonnormal and Non-IID Returns 208 5.7 Implementation of Tests 211 5.7.1 Summary of Empirical Evidence 211 5.7.2 Illustrative Implementation 212 5.7.3 Unobservability of the Market Portfolio 213 5.8 Cross-Sectional Regressions 215 5.9 Conclusion 217 6Multifactor Pricing Models 219 6.1 Theoretical Background 219 6.2 Estimation and Testing 222 6.2.1 Portfolios as Factors with a Riskfree Asset 223 6.2.2 Portfolios as Factors without a Riskfree Asset 224 6.2.3 Macroeconomic Variables as Factors 226 6.2.4 Factor Portfolios Spanning the Mean-Variance\protect\\ Frontier 228 6.3 Estimation of Risk Premia and Expected Returns 231 6.4 Selection of Factors 233 6.4.1 Statistical Approaches 233 6.4.2 Number of Factors 238 6.4.3 Theoretical Approaches 239 6.5 Empirical Results 240 6.6 Interpreting Deviations from Exact Factor Pricing 242 6.6.1 Exact Factor Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfolio 243 6.6.2 Squared Sharpe Ratios 245 6.6.3 Implications for Separating Alternative Theories 246 6.7 Conclusion 251 7Present-Value Relations 253 7.1 The Relation between Prices, Dividends, and Returns 254 7.1.1 The Linear Present-Value Relation with Constant Expected Returns 255 7.1.2 Rational Bubbles 258 7.1.3 An Approximate Present-Value Relation with Time-Varying Expected Returns 260 7.1.4 Prices and Returns in a Simple Example 264 7.2 Present-Value Relations and US Stock Price Behavior 267 7.2.1 Long-Horizon Regressions 267 7.2.2 Volatility Tests 275 7.2.3 Vector Autoregressive Methods 279 7.3 Conclusion 286 8Intertemporal Equilibrium Models 291 8.1 The Stochastic Discount Factor 293 8.1.1 Volatility Bounds 296 8.2 Consumption-Based Asset Pricing with Power Utility 304 8.2.1 Power Utility in a Lognormal Model 306 8.2.2 Power Utility and Generalized Method of\protect\\ Moments 314 8.3 Market Frictions 314 8.3.1 Market Frictions and Hansen-Jagannathan\protect\\ Bounds 315 8.3.2 Market Frictions and Aggregate Consumption\protect\\ Data 316 8.4 More General Utility Functions 326 8.4.1 Habit Formation 326 8.4.2 Psychological Models of Preferences 332 8.5 Conclusion 334 9Derivative Pricing Models 339 9.1 Brownian Motion 341 9.1.1 Constructing Brownian Motion 341 9.1.2 Stochastic Differential Equations 346 9.2 A Brief Review of Derivative Pricing Methods 349 9.2.1 The Black-Scholes and Merton Approach 350 9.2.2 The Martingale Approach 354 9.3 Implementing Parametric Option Pricing Models 355 9.3.1 Parameter Estimation of Asset Price Dynamics 356 9.3.2 Estimating $\sigma $ in the Black-Scholes Model 361 9.3.3 Quantifying the Precision of Option Price Estimators 367 9.3.4 The Effects of Asset Return Predictability 369 9.3.5 Implied Volatility Estimators 377 9.3.6 Stochastic Volatility Models 379 9.4 Pricing Path-Dependent Derivatives Via Monte Carlo Simulation 382 9.4.1 Discrete Versus Continuous Time 383 9.4.2 How Many Simulations to Perform 384 9.4.3 Comparisons with a Closed-Form Solution 384 9.4.4 Computational Efficiency 386 9.4.5 Extensions and Limitations 390 9.5 Conclusion 391 10Fixed-Income Securities 395 10.1 Basic Concepts 396 10.1.1 Discount Bonds 397 10.1.2 Coupon Bonds 401 10.1.3 Estimating the Zero-Coupon Term Structure 409 10.2 Interpreting the Term Structure of Interest Rates 413 10.2.1 The Expectations Hypothesis 413 10.2.2 Yield Spreads and Interest Rate Forecasts 418 10.3 Conclusion 423 11Term-Structure Models 427 11.1 Affine-Yield Models 428 11.1.1 A Homoskedastic Single-Factor Model 429 11.1.2 A Square-Root Single-Factor Model 435 11.1.3 A Two-Factor Model 438 11.1.4 Beyond Affine-Yield Models 441 11.2 Fitting Term-Structure Models to the Data 442 11.2.1 Real Bonds, Nominal Bonds, and Inflation 442 11.2.2 Empirical Evidence on Affine-Yield Models 445 11.3 Pricing Fixed-Income Derivative Securities 455 11.3.1 Fitting the Current Term Structure Exactly 456 11.3.2 Forwards and Futures 458 11.3.3 Option Pricing in a Term-Structure Model 461 11.4 Conclusion 464 12Nonlinearities in Financial Data 467 12.1 Nonlinear Structure in Univariate Time Series 468 12.1.1 Some Parametric Models 470 12.1.2 Univariate Tests for Nonlinear Structure 475 12.2 Models of Changing Volatility 479 12.2.1 Univariate Models 481 12.2.2 Multivariate Models 490 12.2.3 Links between First and Second Moments 494 12.3 Nonparametric Estimation 498 12.3.1 Kernel Regression 500 12.3.2 Optimal Bandwidth Selection 502 12.3.3 Average Derivative Estimators 504 12.3.4 Application: Estimating State-Price Densities 507 12.4 Artificial Neural Networks 512 12.4.1 Multilayer Perceptrons 512 12.4.2 Radial Basis Functions 516 12.4.3 Projection Pursuit Regression 518 12.4.4 Limitations of Learning Networks 518 12.4.5 Application: Learning the Black-Scholes Formula 519 12.5 Overfitting and Data-Snooping 523 12.6 Conclusion 524 Appendix 527 A.1 Linear Instrumental Variables 527 A.2 Generalized Method of Moments 532 A.3 Serially Correlated and Heteroskedastic Errors 534 A.4 GMM and Maximum Likelihood 536 References 541 Author Index 587 Subject Index 597

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Book SynopsisThe book provides insights in the decision-making for implementing strategies in various spheres of real-world issues. It integrates optimal policies in various decision­making problems and serves as a reference for researchers and industrial practitioners. Furthermore, the book provides sound knowledge of modelling of real-world problems and solution procedure using the various optimisation and statistical techniques for making optimal decisions. The book is meant for teachers, students, researchers and industrialists who are working in the field of materials science, especially operations research and applied statistics. Table of Contents1. A New Version of the Generalized Rayleigh Distribution with Copula, Properties, Applications and Different Methods of Estimation 2. Expanding the Burr X Model: Properties, Copula, Real Data Modeling and Different Methods of Estimation 3. Transmuted Burr Type X Model with Applications to Life Time Data 4. Monitoring Patients Blood Level through Enhanced Control Chart 5. Goodness of Fit in Parametric and Non-parametric Econometric Models 6. Stochastic Models for Cancer Progression and its Optimal Programming for Control with Chemotherapy 7. A New Unrelated Question Model with Two Questions Per Card 8. Hybrid of Simple Model and a New Unrelated Question Model for Two Sensitive Characteristics 9. Hybrid of Crossed Model and a New Unrelated Question Model for Two Sensitive Characteristics 10. Modified Regression Type Estimator by Ingeniously Utilizing Probabilities for more Efficient Results in Randomized Response Sampling 11. Ratio and Regression Type Estimators for a New Measure of Coefficient of Dispersion Relative to the Empirical Mode 12. Class of Exponential Ratio Type Estimator for Population Mean in Adaptive Cluster Sampling 13. An Inventory Model for Substitutable Deteriorating Products under Fuzzy and Cloud Fuzzy Demand Rate 14. Co-ordinated Selling Price and Replenishment Policies for Duopoly Retailers under Quadratic Demand and Deteriorating Nature of Items15. Quadratic Programming Approach for the Optimal Multi-objective Transportation Problem 16. Analyzing Multi-Objective Fixed-Charge Solid Transportation Problem under Rough and Fuzzy-Rough Environments 17. Overall Shale Gas Water Management: A Neutrosophic Optimization Approach 18. Memory Effect on an EOQ Model with Price Dependant Demand and Deterioration 19. Optimality Conditions of an Unconstrained Imprecise Optimization Problem via Interval Order Relation 20. Power Comparison of Different Goodness of Fit Tests for Beta Generalized Weibull Distribution 21. On the Transmuted Modified Lindley Distribution: Theory and Applications to Lifetime Data 22. Adjusted Bias and Risk for Estimating Treatment Effect after Selection with an Application in Idiopathic Osteoporosis 23. Validity Judgement of an EOQ Model using Phi-coefficient 24. Uncertain Chance-Constrained Multi-Objective Geometric Programming Problem 25. Optimal Decision Making for the Prediction of Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients

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Book SynopsisThe focus of this book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.Trade Review'Boyd and Vandenberghe have written a beautiful book that I strongly recommend to everyone interested in optimization and computational mathematics: Convex Optimization is a very readable introduction to this modern field of research.' Mathematics of Operations Research'… a beautiful book that I strongly recommend to everyone interested in optimization and computational mathematics … a very readable and inspiring introduction to this modern field of research. I recommend it as one of the best optimization textbooks that have appeared in the last years.' Mathematical Methods of Operations Research'I highly recommend it either if you teach nonlinear optimization at the graduate level for a supplementary reading list and for your library, or if you solve optimization problems and wish to know more about solution methods and applications.' International Statistical institute'… the whole book is characterized by clarity. … a very good pedagogical book … excellent to grasp the important concepts of convex analysis [and] to develop an art in modelling optimization problems intelligently.' Matapli'The book by Boyd and Vandenberghe reviewed here is one of … the best I have ever seen … it is a gentle, but rigorous, introduction to the basic concepts and methods of the field … this book is meant to be a 'first book' for the student or practitioner of optimization. However, I think that even the experienced researcher in the field has something to gain from reading this book: I have very much enjoyed the easy to follow presentation of many meaningful examples and suggestive interpretations meant to help the student's understanding penetrate beyond the surface of the formal description of the concepts and techniques. For teachers of convex optimization this book can be a gold mine of exercises. MathSciNetTable of ContentsPreface; 1. Introduction; Part I. Theory: 2. Convex sets; 3. Convex functions; 4. Convex optimization problems; 5. Duality; Part II. Applications: 6. Approximation and fitting; 7. Statistical estimation; 8. Geometrical problems; Part III. Algorithms: 9. Unconstrained minimization; 10. Equality constrained minimization; 11. Interior-point methods; Appendices.

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Book SynopsisA timely new edition, fully updated and revised,Standard Deviationsdemystifies the science behind the statistics

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Book SynopsisIntroduction to Stochastic Finance with Market Examples, Second Edition presents an introduction to pricing and hedging in discrete and continuous-time financial models, emphasizing both analytical and probabilistic methods. It demonstrates both the power and limitations of mathematical models in finance, covering the basics of stochastic calculus for finance, and details the techniques required to model the time evolution of risky assets. The book discusses a wide range of classical topics including BlackScholes pricing, American options, derivatives, term structure modeling, and change of numéraire. It also builds up to special topics, such as exotic options, stochastic volatility, and jump processes.New to this Edition New chapters on Barrier Options, Lookback Options, Asian Options, Optimal Stopping Theorem, and Stochastic Volatility Contains over 235 exercises and 16 problems with complete solutions available online from the iTable of ContentsIntroduction. 1. Assets, Portfolios, and Arbitrage. 1.1. Portfolio Allocation and Short Selling. 1.2. Arbitrage. 1.3. Risk-Neutral Probability Measures. 1.4. Hedging of Contingent Claims. 1.5. Market Completeness. 1.6. Example: Binary Market. Exercises. 2. Discrete-Time Market Model. 2.1. Discrete-Time Compounding. 2.2. Arbitrage and Self-Financing Portfolios. 2.3. Contingent Claims. 2.4. Martingales and Conditional Expectations. 2.5. Market Completeness and Risk-Neutral Measures. 2.6. The Cox-Ross-Rubinstein (CRR) Market Model. Exercises. 3. Pricing and Hedging in Discrete Time. 3.1. Pricing Contingent Claims. 3.2. Pricing Vanilla Options in the CRR Model. 3.3. Hedging Contingent Claims. 3.4. Hedging Vanilla Options. 3.5. Hedging Exotic Options. 3.6. Convergence of the CRR Model. Exercises. 4. Brownian Motion and Stochastic Calculus. 4.1. Brownian Motion. 4.2. Three Constructions of Brownian Motion. 4.3. Wiener Stochastic Integral. 4.4. Itô Stochastic Integral. 4.5. Stochastic Calculus. Exercises. 5. Continuous-Time Market Model. 5.1. Asset Price Modeling. 5.2. Arbitrage and Risk-Neutral Measures. 5.3. Self-Financing Portfolio Strategies. 5.4. Two-Asset Portfolio Model. 5.5. Geometric Brownian Motion. Exercises. 6. Black-Scholes Pricing and Hedging. 6.1. The Black-Scholes PDE. 6.2. European Call Options. 6.3. European Put Options. 6.4. Market Terms and Data. 6.5. The Heat Equation. 6.6. Solution of the Black-Scholes PDE. Exercises. 7. Martingale Approach to Pricing and Hedging. 7.1. Martingale Property of the Itô Integral. 7.2. Risk-neutral Probability Measures. 7.3. Change of Measure and the Girsanov Theorem. 7.4. Pricing by the Martingale Method. 7.5. Hedging by the Martingale Method. Exercises. 8. Stochastic Volatility. 8.1. Stochastic Volatility Models. 8.2. Realized Variance Swaps. 8.3. Realized Variance Options. 8.4. European Options - PDE Method. 8.5. Perturbation Analysis. Exercises. 9. Volatility Estimation. 9.1. Historical Volatility. 9.2. Implied Volatility. 9.3. Local Volatility. 9.4. The VIX® Index. Exercises. 10. Maximum of Brownian motion. 10.1. Running Maximum of Brownian Motion. 10.2. The Reflection Principle. 10.3. Density of the Maximum of Brownian Motion. 10.4. Average of Geometric Brownian Extrema. Exercises. 11. Barrier Options. 11.1. Options on Extrema. 11.2. Knock-Out Barrier. 11.3. Knock-In Barrier. 11.4. PDE Method. 11.5. Hedging Barrier Options. Exercises. 12. Lookback Options. 12.1. The Lookback Put Option. 12.2. PDE Method. 12.3. The Lookback Call Option. 12.4. Delta Hedging for Lookback Options. Exercises. 13. Asian Options. 13.1. Bounds on Asian Option Prices. 13.2. Hartman-Watson Distribution. 13.3. Laplace Transform Method. 13.4. Moment Matching Approximations. 13.5. PDE Method. Exercises. 14. Optimal Stopping Theorem. 14.1. Filtrations and Information Flow. 14.2. Submartingales and Supermartingales. 14.3. Optimal Stopping Theorem. 14.4. Drifted Brownian Motion. Exercises. 15. American Options. 15.1. Perpetual American Put Options. 15.2. PDE Method for Perpetual Put Options. 15.3. Perpetual American Call Options. 15.4. Finite Expiration American Options. 15.5. PDE Method with Finite Expiration. Exercises. 16. Change of Numéraire and Forward Measures. 16.1. Notion of Numéraire. 16.2. Change of Numéraire. 16.3. Foreign Exchange. 16.4. Pricing Exchange Options. 16.5. Hedging by Change of Numéraire. Exercises. 17. Short Rates and Bond Pricing. 17.1. Vasicek model. 17.2. Affine Short Rate Models. 17.3. Zero-Coupon and Coupon Bonds. 17.4. Bond Pricing PDE. Exercises. 18. Forward Rates. 18.1. Construction of Forward Rates. 18.2. LIBOR/SOFR Swap Rates. 18.3. The HJM Model. 18.4. Yield Curve Modeling. 18.5. Two-Factor Model. 18.6. The BGM Model. Exercises. 19. Pricing of Interest Rate Derivatives. 19.1. Forward Measures and Tenor Structure. 19.2. Bond Options. 19.3. Caplet Pricing. 19.4. Forward Swap Measures. 19.5. Swaption Pricing. Exercises. 20. Stochastic Calculus for Jump Processes. 20.1. The Poisson Process. 20.2. Compound Poisson Process. 20.3. Stochastic Integrals and Itô Formula with Jumps. 20.4. Stochastic Differential Equations with Jumps. 20.5. Girsanov Theorem for Jump Processes. Exercises. 21. Pricing and Hedging in Jump Models. 21.1. Fitting the Distribution of Market Returns. 21.2. Risk-Neutral Probability Measures. 21.3. Pricing in Jump Models. 21.4. Exponential Lévy Models. 21.5. Black-Scholes PDE with Jumps. 21.6. Mean-Variance Hedging with Jumps. Exercises. 22. Basic Numerical Methods. 22.1. Discretized Heat Equation. 22.2. Discretized Black-Scholes PDE. 22.3. Euler Discretization. 22.4. Milshtein Discretization. Exercises. Bibliography. Index

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Book SynopsisThis third edition of Braun and Murdoch''s bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the BoxMuller and MetropolisHastings algorithms. New examples and exercises have been added throughout. This is the only introduction you''ll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical dataTable of Contents1. Getting Started; 2. Introduction to the R Language; 3. Programming Statistical Graphics; 4. Programming with R; 5. Complex Programming in the Tidyverse; 6. Simulation; 7. Computational Linear Algebra; 8. Numerical Optimization; A. Review of Random Variables and Distributions; B. Base Graphics Details; Index.

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Book SynopsisAbout our authors Mark L. Berenson is Professor of Information Management and Business Analytics at Montclair State University and Professor Emeritus of Information Systems and Statistics at Baruch College. He currently teaches graduate and undergraduate courses in statistics and operations management in the School of Business, and an undergraduate course in international justice and human rights that he co-developed in the College of Humanities and Social Sciences. Berenson received a BA in economic statistics and an MBA in business statistics from City College of New York and a PhD in business from the City University of New York. Berenson's research has been published in Decision Sciences Journal of Innovative Education, Review of Business Research, The American Statistician, Communications in Statistics, Psychometrika, Educational and Psychological Measurement, Journal of Management Sciences and Applied Cybernetics, Research Quarterly, Stats Magazine, The New York Statistician, Journal of Health Administration Education, Journal of Behavioral Medicine, and Journal of Surgical Oncology. His invited articles have appeared in The Encyclopedia of Measurement & Statistics and the Encyclopedia of Statistical Sciences. He has coauthored numerous statistics texts published by Pearson. Over the years, Berenson has received several awards for teaching and for innovative contributions to statistics education. In 2005, he was the first recipient of the Catherine A. Becker Service for Educational Excellence Award at Montclair State University and in 2012, he was the recipient of the Khubani/Telebrands Faculty Research Fellowship in the School of Business. David Levine, Professor Emeritus of Statistics and CIS at Baruch College, CUNY, has been a nationally recognized innovator in statistics education for more than 3 decades. Levine has coauthored 14 books, including several business statistics textbooks; textbooks and professional titles that explain and explore quality management and the Six Sigma approach; and, with David Stephan, a trade paperback that explains statistical concepts to a general audience. Levine has presented or chaired numerous sessions about business education at leading conferences conducted by the Decision Sciences Institute (DSI) and the American Statistical Association, and he and his coauthors have been active participants in the annual DSI Data, Analytics, and Statistics Instruction (DASI) mini-conference. During his many years teaching at Baruch College, Levine was recognized for his contributions to teaching and curriculum development with the College's highest distinguished teaching honor. He earned BBA and MBA degrees from CCNY, and a PhD in industrial engineering and operations research from New York University. Kathryn Szabat, Associate Professor of Business Systems and Analytics at La Salle University, has transformed several business school majors into 1 interdisciplinary major that better supports careers in new and emerging disciplines of data analysis, including analytics. Szabat strives to inspire, stimulate, challenge and motivate students through innovation and curricular enhancements, and shares her coauthors' commitment to teaching excellence and the continual improvement of statistics presentations. Beyond the classroom, she has provided statistical advice to numerous business, non-business and academic communities, with particular interest in the areas of education, medicine, and nonprofit capacity building. Her research activities have led to journal publications, chapters in scholarly books, and conference presentations. Szabat is a member of the American Statistical Association (ASA), DSI, Institute for Operation Research and Management Sciences (INFORMS), and DSI DASI. She received a BS from SUNY-Albany, an MS in statistics from the Wharton School of the University of Pennsylvania, and a PhD degree in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania. David Stephan's professional life has always been shaped by advances in computing. As an undergraduate, he helped professors use statistics software that was considered advanced, even though it could compute only several things discussed in Chapter 3, thereby gaining an early appreciation for the benefits of using software to solve problems (and perhaps positively influencing his grades). An early advocate of using computers to support instruction, he developed a prototype of a mainframe-based system that anticipated features found today in Pearson's MathXL, and served as special assistant for computing to the Dean and Provost at Baruch College. In his many years teaching at Baruch, Stephan implemented the first computer-based classroom; helped redevelop the CIS curriculum; and as part of a FIPSE project team, designed and implemented a multimedia learning environment. He was also nominated for teaching honors. Stephan has presented at SEDSI and DSI DASI (formerly MSMESB) mini-conferences, sometimes with his coauthors. Stephan earned a BA from Franklin & Marshall College and an MS from Baruch College, CUNY, and completed the instructional technology graduate program at Teachers College, Columbia University.

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Book SynopsisThis trusted textbook returns in its 4th edition with even more exercises to help consolidate understanding - and a companion website featuring additional materials, including a solutions manual for instructors. Offering a unique blend of theory and practical application, it provides ideal preparation for doing applied econometric work as it takes students from a basic level up to an advanced understanding in an intuitive, step-by-step fashion. Clear presentation of economic tests and methods of estimation is paired with practical guidance on using several types of software packages. Using real world data throughout, the authors place emphasis upon the interpretation of results, and the conclusions to be drawn from them in econometric work. This book will be essential reading for economics undergraduate and master's students taking a course in applied econometrics. Its practical nature makes it ideal for modules requiring a research project. New to this Edition:- Additional practical eTable of ContentsPART I: STATISTICAL BACKGROUND AND BASIC DATA HANDLING 1. Fundamental Concepts 2. The Structure Of Economic Data and Basic Data Handling PART II: THE CLASSICAL LINEAR REGRESSION MODEL 3. Simple Regression 4. Multiple Regression PART III: VIOLATING THE ASSUMPTIONS OF THE CLRM 5. Multicollinearity 6. Heteroskedasticity 7. Autocorrelation 8. Misspecification: Wrong Regressors, Measurement Errors And Wrong Functional Forms PART IV: TOPICS IN ECONOMETRICS 9. Dummy Variables 10. Dynamic Econometric Models 11. Simultaneous Equation Models 12. Limited Dependent Variable Regression Models PART V: TIME SERIES ECONOMETRICS 13. ARIMA Models And The Box–Jenkins Methodology 14. Modelling The Variance: ARCH–GARCH Models 15. Vector Autoregressive(VAR) Models And Causality Tests 16. Non-Stationarity and Unit Root Tests 17. Cointegration and Error-Correction Models 18. Identification In Standard and Cointegrated Systems 19. Solving Models 20. Time Varying Coefficient Models: A New Way of Estimating Bias Free Parameters PART VI: PANEL DATA ECONOMETRICS 21. Traditional Panel Data Models 22. Dynamic Heterogeneous Panels 23. Non-Stationary Panels PART VII: USING ECONOMETRIC SOFTWARE 24. Practicalities in Using Eviews and Stata.

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Book SynopsisThe World''s Greatest Book on Successful Living-Now in a Special Compact Edition!Here is the complete experience of Think and Grow Rich in an exquisitely brief and faithful condensation. In less than an hour of reading you will learn all thirteen of Napoleon Hill''s famous steps to wealth and achievement. This masterly summation of Hill''s original landmark explains: * Why you must write down your goals.* The immeasurable importance of a definite major aim.* How to benefit from hunches and sudden inspirations.* The magic of persistence in the face of setbacks.* How to program your mind for success. * The extraordinary power of a "Master Mind" group. Abridged and introduced by PEN Award-winning historian Mitch Horowitz, this concise rendition of Hill''s masterwork is both the perfect introduction to Think and Grow Rich and a great refresher for those who already know the book and its powers.Napoleon Hill was born in Wise County, Virginia. He began his writing career at age 13 as a "mountain reporter" for small town newspapers and went on to become America''s most beloved motivational author. His work stands as a monument to individual achievement and is the cornerstone of modern motivation. His most famous work, Think and Grow Rich, is one of the best-selling books of all time. Hill established the Foundation as a nonprofit educational institution whose mission is to perpetuate his philosophy of leadership, self-motivation, and individual achievement.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisUsing a reader-friendly and straightforward approach, Langley/Novack/Gibson/Coyle's SUPPLY CHAIN MANAGEMENT: A LOGISTICS PERSPECTIVE, Cengage International Edition, 12th Edition, blends logistics theory with practical applications. The latest content highlights emerging issues, technology developments and global changes in the constantly evolving and critically-important field of supply chain management. This edition examines today's real companies and how public and private organizations are responding to the continuing pressure to modernize and transform their supply chains. Dive into real-world businesses and see how organizations respond to the ongoing need for modernization in their supply chains. Experience hands-on learning with updated features, brief cases and global content -- providing a glimpse into the decisions supply chain managers make daily. Each chapter begins with profiles that spotlight real organizations, people or events -- underlining the significance of current supply chain matters.

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Book SynopsisTable of ContentsPART I: EXPLORING AND COLLECTING DATA 1. Data and Decisions (H&M) 2. Visualizing and Describing Categorical Data (Dalia Research) 3. Describing, Displaying, and Visualizing Quantitative Data (AIG) 4. Correlation and Linear Regression (Zillow.com) PART II: MODELING AND PROBABILITY 5. Randomness and Probability (Credit Reports, the Fair Isaacs Corporation, and Equifax) 6. Random Variables and Probability Models (Metropolitan Life Insurance Company) 7. The Normal and Other Continuous Distributions (The NYSE) PART III: GATHERING DATA 8. Data Sources: Observational Studies and Surveys (Roper Polls) 9. Data Sources: Experiments (Capital One) PART IV: INFERENCE FOR DECISION MAKING 10. Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story) 11. Confidence Intervals for Means (Guinness & Co.) 12. Testing Hypotheses (Casting Ingots) 13. More About Tests and Intervals (Traveler's Insurance) 14. Comparing Two Means (Visa Global Organization) 15. Inference for Counts: Chi-Square Tests (SAC Capital) PART V: MODELS FOR DECISION MAKING 16. Inference for Regression (Nambé Mills) 17. Understanding Residuals (Kellogg's) 18. Multiple Regression (Zillow.com) 19. Building Multiple Regression Models (Bolliger and Mabillard) 20. Time Series Analysis (Whole Foods Market®) PART VI: ANALYTICS 21. Introduction to Big Data and Data Mining (Paralyzed Veterans of America) PART VII: ONLINE TOPICS 22. Quality Control (Sony) 23. Nonparametric Methods (i4cp) 24. Decision Making and Risk (Data Description, Inc.) 25. Analysis of Experiments and Observational Studies Appendix A. Answers Appendix B. Tables and Selected Formulas Appendix C. Credits

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Book SynopsisDavid M. Levine and David F. Stephan are part of a writing team known for their series of business statistics textbooks that include Basic Business Statistics, Business Statistics: A First Course, and Statistics for Managers Using Microsoft Excel. In long teaching careers at Baruch College, both were known for their classroom innovations, with Levine being honored with a Presidential Excellence Award for Distinguished Teaching Award and Stephan granted the privilege to design and develop the College's first computer-based classroom. Both are active members of the Data, Analytics and Statistics Instruction SIG of the Decision Sciences Institute. Levine is Professor Emeritus of Information Systems at Baruch College. He is nationally recognized innovator in business statistics education and is also the coauthor of Applied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab. Levine is also the authorTable of ContentsIntroduction The Even You Can Learn Statistics and Analytics Owner's Manual. xiii Chapter 1 Fundamentals of Statistics. 1 1.1 The First Three Words of Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 The Fourth and Fifth Words. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 The Branches of Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Sources of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Sampling Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.6 Sample Selection Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 2 Presenting Data in Tables and Charts . 15 2.1 Presenting Categorical Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Presenting Numerical Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 “Bad” Charts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Chapter 3 Descriptive Statistics. 45 3.1 Measures of Central Tendency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 Measures of Position. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 Measures of Variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.4 Shape of Distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Chapter 4 Probability. 75 4.1 Events. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.2 More Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.3 Some Rules of Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.4 Assigning Probabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Chapter 5 Probability Distributions. 87 5.1 Probability Distributions for Discrete Variables. . . . . . . . . . . . . . . . . . . . . . . . 87 5.2 The Binomial and Poisson Probability Distributions. . . . . . . . . . . . . . . . . . . . 93 5.3 Continuous Probability Distributions and the Normal Distribution . . . . . . . 100 5.4 The Normal Probability Plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Chapter 6 Sampling Distributions and Confidence Intervals. 121 6.1 Foundational Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.2 Sampling Error and Confidence Intervals. . . . . . . . . . . . . . . . . . . . . . . . . . . 125 6.3 Confidence Interval Estimate for the Mean Using the t Distribution (? Unknown). . . 128 6.4 Confidence Interval Estimation for Categorical Variables . . . . . . . . . . . . . . . 131 6.5 Confidence Interval Estimation When Normality Cannot Be Assumed. . . . . 134 Chapter 7 Fundamentals of Hypothesis Testing. 145 7.1 The Null and Alternative Hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.2 Hypothesis Testing Issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 7.3 Decision-Making Risks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 7.4 Performing Hypothesis Testing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 7.5 Types of Hypothesis Tests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Chapter 8 Hypothesis Testing: Z and t Tests. 157 8.1 Test for the Difference Between Two Proportions . . . . . . . . . . . . . . . . . . . . . 157 8.2 Test for the Difference Between the Means of Two Independent Groups . . . . 163 8.3 The Paired t Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Chapter 9 Hypothesis Testing: Chi-Square Tests and the One-Way Analysis of Variance (ANOVA). 183 9.1 Chi-Square Test for Two-Way Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 9.2 One-Way Analysis of Variance (ANOVA): Testing for the Differences Among the Means of More Than Two Groups. . . . . . . . . . . . . . . 191 Chapter 10 Simple Linear Regression. 211 10.1 Basics of Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 10.2 Developing a Simple Linear Regression Model. . . . . . . . . . . . . . . . . . . . . . 214 10.3 Measures of Variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 10.4 Inferences About the Slope. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 10.5 Common Mistakes When Using Regression Analysis . . . . . . . . . . . . . . . . . 229 Chapter 11 Multiple Regression. 243 11.1 The Multiple Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 11.2 Coefficient of Multiple Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 11.3 The Overall F Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 11.4 Residual Analysis for the Multiple Regression Model . . . . . . . . . . . . . . . . . 247 11.5 Inferences Concerning the Population Regression Coefficients. . . . . . . . . . 248 Chapter 12 Introduction to Analytics. 259 12.1 Basic Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 12.2 Descriptive Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 12.3 Typical Descriptive Analytics Visualizations. . . . . . . . . . . . . . . . . . . . . . . . 269 Chapter 13 Predictive Analytics. 279 13.1 Predictive Analytics Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 13.2 More About Predictive Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 13.3 Tree Induction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 13.4 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 13.5 Association Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 Appendix A Microsoft Excel Operation and Configuration . 299 Appendix B Review of Arithmetic and Algebra. 301 Appendix C Statistical Tables. 311 Appendix D Spreadsheet Tips . 339 Appendix E Advanced Techniques. 343 Appendix F Documentation for Downloadable Files. 353 9780137654765, TOC, 4/25/2022

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Book SynopsisWhy did the size of the U.S. economy increase by 3 percent on one day in mid-2013 - or Ghana's balloon by 60 percent overnight in 2010? Why did the U.K. financial industry show its fastest expansion ever at the end of 2008 - just as the world's financial system went into meltdown? This title deals with these questions.Trade ReviewWinner of the 2015 Bronze Medal in Economics, Axiom Business Book Awards One of The Wall Street Journal's Best Books of 2014 One of Choice's Outstanding Academic Titles for 2014 One of FA-mag.com's Books of the Year 2014 One of "The Books Quartz Read" in 2014 One of Minnpost.com's 'Three (plus) books for the econ buff on your list' 2014 Longlisted for the Financial Times and McKinsey Business Book of the Year 2014 "GDP is, as Diane Coyle points out in her entertaining and informative GDP: A Brief but Affectionate History, a bodge, an ongoing argument."--John Lanchester, London Review of Books "[A] little charmer of a book... GDP: A Brief but Affectionate History is just what the title promises... Cowperthwaite himself would nod in agreement over Ms. Coyle's informed discussion of what the GDP misses and how it misfires... Ms. Coyle--a graceful and witty writer, by the way--recounts familiar problems and adds some new ones... [E]xcellent."--James Grant, Wall Street Journal "Anyone who wants to know how GDP and the SNA have come to play such important roles in economic policy-making will gain from reading Coyle's book. As will anyone who wants to gain more understanding of the concept's strengths and weaknesses."--Nicholas Oulton, Science "Diane Coyle's new book, GDP: A Brief But Affectionate History, is a timely contribution to discussions of modern economic performance."--Arnold Kling, American "[E]xcellent."--Adam Creighton, The Australian "Diane Coyle's book is as good a simple guide as we are likely to see."--Samuel Brittan, Financial Times "Coyle does good work explicating a topic that few understand, even if it affects each of us daily. A pleasure for facts-and-numbers geeks, though accessibly written and full of meaningful real-world examples."--Kirkus Reviews "[S]mart and lucid... [S]hort but masterful."--Todd G. Buchholz, Finance & Development "[G]reat (and well-timed) new book."--Uri Friedman, The Atlantic "In a charming and accessible new book, Diane Coyle untangles the history, assumptions, challenges and shortcomings of this popular rhetorical device, which has become so central to policy debates around the world... Coyle's book is a good primer for the average citizen as well as the seasoned economist."--Adam Gurri, Umlaut "[I]t is interesting and important, particularly when it comes to the emphasis now given to GDP, and the inadequacies of this now time-honoured measurement of how our economies are doing... With clarity and precision, she explains its strengths and weaknesses."--Peter Day, BBC News Business "Diane Coyle has bravely attempted in a recent book to make the subject once more accessible, and even interesting."--John Kay, Financial Times "[T]his is as engaging a book about GDP as you could ever hope to read. It falls into that genre of books that are 'biographies of things'--be they histories of longitude, the number zero or the potato--and is both enlightening and entertaining."--Andrew Sawers, FS Focus "GDP: A Brief But Affectionate History is a fascinating 140-page book that I cannot recommend highly enough. This is simply the best book on GDP that I've ever seen."--John Mauldin "As a potted history of approaches to quantifying national output from the 18th century onward, GDP: A Brief but Affectionate History deserves high marks. It is particularly edifying to learn about the military motivation behind the initial attempts."--Martin S. Fridson, Financial Analysts Journal "The strongest part of the book charts the development of national accounting from the 17th century through to the creation of GDP itself and its literal and metaphorical rises and falls in the 20th and 21st centuries... This is lively and surprisingly readable stuff."--Eilis Lawlor, LSE Review of Books "Coyle has written an engaging, introductory to mid-level book on the GDP that makes sense of a statistic that hardly anyone actually understands... It does not require any training in economics, but it covers many topics that even professional economists would find beneficial, including an argument that GDP is an increasingly inappropriate measure for the 21st century."--Choice "[A] little charmer of a book."--Wall Street Journal (A Best Non-Fiction Book of 2014) "GDP is a thought-provoking account of how the gross domestic product statistic came to be so important... The book is a useful and timely contribution."--Louise Rawlings, Economic RecordTable of ContentsNote on the Paperback Edition vii Introduction 1 ONE From the Eighteenth Century to the 1930s: War and Depression 7 TWO 1945 to 1975: The Golden Age 43 THREE The Legacy of the 1970s: A Crisis of Capitalism 61 FOUR 1995 to 2005: The New Paradigm 79 FIVE Our Times: The Great Crash 95 SIX The Future: Twenty-first-Century GDP 123 Acknowledgments 147 Notes 149 Index 161

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Book SynopsisThis book shows how our lives are shaped not only by the choices we make, but by the choices we have.From dating, school and university applications to the job market, understand the most important decisions you'll ever make with insights from a Nobel Prize-winner.Who Gets What and Why is a piquantly written, mind-expanding exploration of the markets that matter most to many of us. If you've ever sought a job or hired someone, applied to university or guided your child into a good school, asked someone out on a date or been asked out, you have participated in a matching market. They are everywhere around us and account for some of the biggest technological successes of the decade, like Uber and Airbnb. Matching markets can even be the gatekeeper of life itself, guiding how desperately ill patients receive scarce organs for transplants.Alvin E. Roth shared the 2012 Nobel Prize in economics for his pioneering research into market design the principles that govern all kinds of markets whTrade Review‘Who Gets What is a pleasure to read. It’s also a pleasure to discover that rare species, a humble economist. Humble but useful’ Wall Street Journal ‘In his fluent and accessible book, Roth vividly describes the successes of market design … Roth discusses a huge array of fascinating market designs: from auctions for the sale of radio spectrum to high-frequency trading; from apartment lettings on Airbnb to sorority rushes; from the allocation of prestigious clerkships to the assignment of public-school places. Mr Roth has not managed to improve all of them, and his descriptions of failures are often as illuminating as the success stories’ Economist ‘An exciting practical approach to economics that enables both individuals and institutions to achieve their goals without running afoul of the profit motive’ Kirkus ‘This book’s lessons are practical as well as theoretical. Understanding how matching markets operate can help readers navigate them more effectively. A solid match for readers in general economics and business collections’ Library Journal ‘He's perfectly brilliant and everyone knows it. But Roth is also an articulate and witty scientist with an impish sense of humour who can make even the most complicated idea seem so simple that you wonder why you didn't think of it yourself. Roth's thinking has revolutionised the design of the ‘matching systems’’ Daniel Gilbert, author of the best-selling Stumbling on Happiness ‘Al Roth is a brilliantly original thinker, but he has spent his entire career solving real, practical problems. He designs markets, but has thought harder than anyone about the ethics and emotions of the marketplace. What's more, he saves lives. Not in some abstract sense: he has saved the lives of real people, and you can shake their hands, look them in the eye, and hear them tell you how he did it. We need more economists like him’ Tim Harford, author of The Undercover Economist

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Book SynopsisEconomic forecasting is a key ingredient of decision making both in the public and in the private sector. Because economic outcomes are the result of a vast, complex, dynamic and stochastic system, forecasting is very difficult and forecast errors are unavoidable.Because forecast precision and reliability can be enhanced by the use of proper econometric models and methods, this innovative book provides an overview of both theory and applications. Undergraduate and graduate students learning basic and advanced forecasting techniques will be able to build from strong foundations, and researchers in public and private institutions will have access to the most recent tools and insights. Readers will gain from the frequent examples that enhance understanding of how to apply techniques, first by using stylized settings and then by real data applications--focusing on macroeconomic and financial topics. This is first and foremost a book aimed at applying time series methods to solve real-world forecasting problems. Applied Economic Forecasting using Time Series Methods starts with a brief review of basic regression analysis with a focus on specific regression topics relevant for forecasting, such as model specification errors, dynamic models and their predictive properties as well as forecast evaluation and combination. Several chapters cover univariate time series models, vector autoregressive models, cointegration and error correction models, and Bayesian methods for estimating vector autoregressive models. A collection of special topics chapters study Threshold and Smooth Transition Autoregressive (TAR and STAR) models, Markov switching regime models, state space models and the Kalman filter, mixed frequency data models, nowcasting, forecasting using large datasets and, finally, volatility models. There are plenty of practical applications in the book and both EViews and R code are available online.Trade Review"This book, by two masters of applied time-series forecasting, is modern, well-balanced, and insightful. And special chapters on things like forecasting in Big Data and/or mixed-frequency data environments enhance its appeal, as does the full set of EViews and R code supplied. Applied Economic Forecasting using Time Series Methods will be an invaluable resource for students and practitioners alike." - Francis X. Diebold, Paul F. and Warren S. Miller Professor of Economics; Professor of Finance and Statistics, University of Pennsylvania "This book is highly welcome as it shows how forecasting is done in practice. For instance, how to use bridge models for now-casting GDP and a (B)VAR model to project it over the next few quarters. Eric Ghysels and Massimiliano Marcellino adopt a pragmatic approach to introduce the time-series models most widely used in forecasting." - Marco Buti, Director-General for Economic and Financial Affairs at the European Commission "Forecasting economic activity and inflation is at the core of monetary policy analysis in central banks. This book combines a very clear exposition of both basic and advanced time series models for forecasting with plenty of practical examples ready for use by practitioners. It's an excellent introduction to the world of forecasting." - Frank Smets, Director General Economics, European Central Bank "This excellent book offers a hands-on introduction to students and researchers with an interest in economic forecasting. It offers in-depth treatments of topics not covered by most textbooks, notably forecasting with mixed-frequency data and nonlinear models, and is full of useful empirical examples. The book is a pleasure to read and highly recommended for anyone with an interest in understanding the nuts and bolts of economic forecasting." - Allan Timmermann, Atkinson/Epstein Endowed Chair Professor of Finance, Rady School of Management, Professor of Finance and Economics, University of California, San DiegoTable of ContentsPreface PART I: Forecasting with the Linear Regression Model Chapter 1 -The Baseline Linear Regression Model Chapter 2 - Model Mis-Specification Chapter 3 - The Dynamic Linear Regression Model Chapter 4 - Forecast Evaluation and Combination PART II: Forecasting with Time Series Models Chapter 5 - Univariate Time Series Models Chapter 6 - VAR Models Chapter 7 - Error Correction Models Chapter 8 - Bayesian VAR Models PART III: TAR, Markov Switching and State Space Models Chapter 9 - TAR and STAR Models Chapter 10 - Markov Switching Models Chapter 11 - State Space Models and the Kalman Filter PART IV: Mixed Frequency, Large Datasets and Volatility Chapter 12 - Models for Mixed Frequency Data Chapter 13 - Models for Large Datasets Chapter 14 - Forecasting Volatility

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Book SynopsisMaths for Economics provides a comprehensive and solid foundation in core mathematical principles and methods used in economics, beginning with revisiting basic skills in arithmetic, algebra, equation solving, and slowly building to more advanced topics. Suitable for those with a range of prior school-level expereince or more generally for those who feel they need to go back to the very basics, students can learn with confidence. Drawing on his extensive experience of teaching in the area, the author appreciates that maths can be a daunting topic for many. As such the text is fully supports the reader by using a combination of engaging learning features including summary sections, examples to show how theory is used in practice and progress exercises, which encourage independent study. Each chapter ends with a conclusion check list to allow students to reflect on topics as they master them.Digital formats and resourcesThe fifth edition is available for students and institutions to purchase in a variety of formats, and is supported by online resources. The e-book offers a mobile experience and convenient access along with functionality tools, navigation features, and links that offer extra learning support: www.oxfordtextbooks.co.uk/ebooks Online resources supporting the book include,For Students: - Ask the author forum - Excel tutorial - Maple tutorial - Further exercises - Answers to further questions - Expanded solutions to progress exercises For Lecturers: - Test exercises - Graphs from the book - Answers to test exercisesTable of ContentsPart One: Foundations 1: Arithmetic 2: Algebra 3: Linear equations 4: Quadratic equations 5: Some further equations and techniques Part Two: Optimization With One Independent Variable 6: Derivatives and differentiation 7: Derivatives in action 8: Economic applications of functions and derivatives 9: Elasticity Part Three: Mathematics Of Finance And Growth 10: Compound growth and present discounted value 11: The exponential function and logarithms 12: Continuous growth and the natural exponential function 13: Derivatives of exponential and logarithmic functions and their applications Part Four: Optimization With Two Or More Independent Variables 14: Functions of two or more independent variables 15: Maximum and minimum values, the total differential, and applications 16: Constrained maximum and minimum values 17: Returns to scale and homogenous functions; partial elasticities; growth accounting; logarithmic scales Part Five: Some Further Topics 18: Integration 19: Matrix algebra 20: Difference and differential equations 21: W21:Extensions and future directions

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Book SynopsisAn introduction to the use of probability models for analyzing risk and economic decisions, using spreadsheets to represent and simulate uncertainty.This textbook offers an introduction to the use of probability models for analyzing risks and economic decisions. It takes a learn-by-doing approach, teaching the student to use spreadsheets to represent and simulate uncertainty and to analyze the effect of such uncertainty on an economic decision. Students in applied business and economics can more easily grasp difficult analytical methods with Excel spreadsheets. The book covers the basic ideas of probability, how to simulate random variables, and how to compute conditional probabilities via Monte Carlo simulation. The first four chapters use a large collection of probability distributions to simulate a range of problems involving worker efficiency, market entry, oil exploration, repeated investment, and subjective belief elicitation. The book then covers correlation and

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Book SynopsisThe second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated.The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis.Econometric Analysis of Cross Section and Panel Dat

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Book SynopsisMaster key spreadsheet and business analytics skills with SPREADSHEET MODELING AND DECISION ANALYSIS: A PRACTICAL INTRODUCTION TO BUSINESS ANALYTICS, 9E, written by respected business analytics innovator Cliff Ragsdale. This edition's clear presentation, realistic examples, fascinating topics and valuable software provide everything you need to become proficient in today's most widely used business analytics techniques using the latest version of Excel in Microsoft Office 365 or Office 2019. Become skilled in the newest Excel functions as well as Analytic Solver and Data Mining add-ins. This edition helps you develop both algebraic and spreadsheet modeling skills. Step-by-step instructions and annotated, full-color screen images make examples easy to follow and show you how to apply what you learn about descriptive, predictive and prescriptive analytics to real business situations. WebAssign online tools and author-created videos further strengthen understanding.Table of Contents1. Introduction to Modeling and Decision Analysis. 2. Introduction to Optimization and Linear Programming. 3. Modeling and Solving LP Problems in a Spreadsheet. 4. Sensitivity Analysis and the Simplex Method. 5. Network Modeling. 6. Integer Linear Programming. 7. Goal Programming and Multiple Objective Optimization. 8. Nonlinear Programming & Evolutionary Optimization. 9. Regression Analysis. 10. Data Mining. 11. Time Series Forecasting. 12. Introduction to Simulation. 13. Queuing Theory. 14. Decision Analysis. 15. Project Management (Online).

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Book SynopsisMaster the proven, traditional methods in project management as well as the latest agile practices with Kloppenborg/Anantatmula/Wells' CONTEMPORARY PROJECT MANAGEMENT, 5E. This edition presents project management techniques and expert examples drawn from successful practice and the latest research. All content reflects the knowledge areas and processes of the 6th edition of the PMBOK Guide as well as the domains and principles of the 7th edition of the PMBOK Guide. The book's focused approach helps you build a strong portfolio to showcase project management skills. New features, glossary and an integrated case highlight agile practices, mindset and techniques, while PMP-style questions prepare you for the new 2021 PMP certification exam. You also learn to use Microsoft Project to automate processes. Gain the expertise you need to become a Certified Associate in Project Management (CAPM) or Certified Project Management Professional (PMP) with this edition and MindTap digital resources.Table of ContentsPart I: ORGANIZING PROJECTS. 1. Introduction to Project Management. 2. Project Selection and Prioritization. 3. Chartering Projects. Part II: LEADING PROJECTS. 4. Organizational Capability: Structure, Culture, and Roles. 5. Leading and Managing Project Teams. 6. Stakeholder Analysis and Communication Planning. Part III: PLANNING PROJECTS. 7. Holistic Scope Planning. 8. Scheduling Projects. 9. Resourcing and Accelerating Projects. 10. Budgeting Projects. 11. Project Uncertainty Planning. 12. Project Quality Planning and Project Kick-off. Part IV: PERFORMING PROJECTS. 13. Project Procurement and Partnering. 14. Determining Project Progress and Results. 15. Finishing the Project and Realizing the Benefits. Appendix A: PMP and CAPM Exam Prep Suggestions Appendix B: PMP Exam Content 2021 Outline with CPM 5e Chapter Coverage Appendix C: Answers to Selected Exercises Appendix D: Project Deliverables Appendix E: Strengths Themes as Used in Project Management Glossary Terms consistent the PMBOK�� 6e and 7e Guides; multiple other PMI Guides and Standards; and current agile practice. Index.

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Book SynopsisWinner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.Features Integrates working code into the main text. Illustrates concepts through worked data analysis examples. Emphasizes understanding assumptions and how assumptions are reflected in code. Offers more detailed explanations of the mathematics in optional sections. Presents examples of using the dagitty R package to analyze causal graphs. Provides the rethinking R package on the author's website and on GitHub. Trade Review"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing."- Adam Loy, Carleton College"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook."- Benjamin K. Goodrich, Columbia University"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory."- Josep Fortiana Gregori, University of Barcelona"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process." - Nguyet Nguyen, Youngstown State University "As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new."- Nathan Green, Journal of the Royal Statistical Society, 2021, https://doi.org/10.1111/rssa.12755"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques."- Abhirup Mallik in Technometrics, August 2021"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing."~Adam Loy, Carleton College"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook."~Benjamin K. Goodrich, Columbia University"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory."~Josep Fortiana Gregori, University of Barcelona"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process." ~Nguyet Nguyen, Youngstown State University"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques."~Abhirup Mallik in Technometrics, August 2021"As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new."~ Nathan Green, Journal of the Royal Statistical Society, 2021Table of Contents1. The Golem of Prague. 2. Small Worlds and Large Worlds. Chapter 3. Sampling the Imaginary. 4. Geocentric Models. 5. The Many Variables & The Spurious Waffles. 6. The Haunted DAG & The Causal Terror. 7. Ulysses’ Compass. 8. Conditional Manatees. 8. Conditional Manatees. 9. Markov Chain Monte Carlo. 10. Big Entropy and the Generalized Linear Model. 11. God Spiked the Integers. 12. Monsters and Mixtures. 13. Models With Memory. 14. Adventures in Covariance. 15. Missing Data and Other Opportunities. 16. Generalized Linear Madness. 17. Horoscopes.

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Book SynopsisFrom a review of the first edition: Modern Data Science with R is rich with examples and is guided by a strong narrative voice. What's more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician).Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to undTrade Review"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." (Hunter Glanz Cal Poly San Luis Obispo)"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." (Lynn Collen, St. Cloud Stat Univ.)"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." (Matthew Beckman, Penn State University)"[...] To answer a wide range of modern research questions, this book by Baumer, Kaplan, and Horton features an excellent introduction to data wrangling, visualization, statistical modeling, machine learning, and other advanced statistical applications through the RStudio environment following the tidyverse syntax. [...] Overall, Modern Data Science with R, 2nd edition serves as an excellent introductory resource to help develop techniques to extract, transform, visualize, and learn from datasets through the R environment. It focuses on implementing those techniques in R and does not provide a theoretical background for the discussed methods. The book will be a perfect reference for a broad audience ranging from undergraduates in data science courses to advanced graduate students and professionals from a variety of research fields."-Kohma Arai and Vyacheslav Lyubchich, in Technometrics, July 2022"Overall, I enjoyed reading this book. The authors were very good at creating a complete tool for studying data science. Therefore, I recommend this book, for its content, writing, and organization, to graduate students in data science and statistics. I also recommend the book to professionals who should prepare themselves for the challenges they are going to face in the future with the voluminous and heterogenous amount of data that should be timely analyzed to extract meaningful information to guide action."-Georgios Nikolopoulos, in ISCB News, June 2022"The authors have successfully completed the job of choosing the content with relevant topics and, deciding the extent of knowledge to be delivered, and finally, putting them in an understandable sequence. This is a well-written book and does not cover much theory. .. The book’s second edition contents are updated, expanded, revised, split, rewritten and rearranged compared to the first edition. The key changes are the use of recently developed R packages, .... (and) updated exercises in the chapters ..."-Shalabh,in Journal of the Royal Statistical Society Series A, August 2021"[This book] provides an excellent basis for statisticians who want to dig deeper into, for example, data handling, for computer scientists who aim to strengthen their knowledge of statistical methods as well as for all other researchers who are interested in data science in general. ... Each section is structured as an interplay between R-code and explanatory text for understanding. The division into several stand-alone segments is an advantage, because the reader may easily choose the section she or he is interested in without missing relevant information. A key feature of the book is its focus on different example data sets that are available via R-packages or from URLs that are embedded in the text. These data sets are used to illustrate the methodology presented using R-code. Their availability allows the reader to reproduce the code while working with the book. ... It can be warmly recommended to practical researchers who seek a comprehensive overview of different topics in data science with focus on implementations in R."-Annika Hoyer, in Biometrical Journal, August 2021"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." -Hunter Glanz, Cal Poly San Luis Obispo"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." -Lynn Collen, St. Cloud Stat University"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." -Matthew Beckman, Penn State University"The authors have covered almost all aspects of data science, a revolutionary field that marries elements of computational thinking and traditional statistical theory. The book can thus equip the readers with the necessary knowledge and skills to extract data from a variety of sources, restructure observations in a form that allows analysis, store data in efficient databases, and work effectively on massive and complex data sets in order to produce actionable information."- Georgios Nikolopoulos, University of Cyprus, ISCB Book Reviews, June 2022.Table of ContentsI Part I: Introduction to Data Science. 1. Prologue: Why data science? 2. Data visualization. 3. A grammar for graphics. 4. Data wrangling on one table. 5. Data wrangling on multiple tables. 6. Tidy data. 7. Iteration. 8. Data science ethics. II. Part II: Statistics and Modeling. 9. Statistical foundations. 10. Predictive modeling. 11. Supervised learning. 12. Unsupervised learning. 13. Simulation. III Part III: Topics in Data Science. 14. Dynamic and customized data graphics. 15. Database querying using SQL. 16. Database administration. 17. Working with spatial data. 18.Geospatial computations. 19. Text as data. 20. Network science. IV Part IV: Appendices.

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Book SynopsisIntroduction to Econometric Modelling provides an introduction to econometrics for undergraduate students. In this book, Gary Koop provides a broader set of models than is offered in existing textbooks and places greater focus on models (e.g. the regression model) than the methods that are used to analyze the models.Trade Review“An introductory text offering econometric methodology for quantifying and managing this variety of risk, illustrated by empirical examples.” (Times Higher Education Supplement, Thursday 28th February)Table of ContentsPreface ix Chapter 1 An Overview of Econometrics 1 1.1 The importance of econometrics 1 1.2 Types of economic data 2 1.3 Working with data: graphical methods 6 1.4 Working with data: descriptive statistics and correlation 11 1.5 Chapter summary 26 Exercises 26 Chapter 2 A Non-technical Introduction to Regression 29 2.1 Introduction 29 2.2 The simple regression model 30 2.3 The multiple regression model 42 2.4 Chapter summary 55 Exercises 57 Chapter 3 The Econometrics of the Simple Regression Model 59 3.1 Introduction 59 3.2 A review of basic concepts in probability in the context of the regression model 60 3.3 The classical assumptions for the regression model 64 3.4 Properties of the ordinary least-squares estimator of β 67 3.5 Deriving a confidence interval for β 75 3.6 Hypothesis tests about β 77 3.7 Modifications to statistical procedures when σ2 is unknown 78 3.8 Chapter summary 81 Exercises 82 Appendix 1: Proof of the Gauss–Markov theorem 84 Appendix 2: Using asymptotic theory in the simple regression model 85 Chapter 4 The Econometrics of the Multiple Regression Model 91 4.1 Introduction 91 4.2 Basic results for the multiple regression model 92 4.3 Issues relating to the choice of explanatory variables 96 4.4 Hypothesis testing in the multiple regression model 102 4.5 Choice of functional form in the multiple regression model 109 4.6 Chapter summary 115 Exercises 116 Appendix: Wald and Lagrange multiplier tests 117 Chapter 5 The Multiple Regression Model: Freeing Up the Classical Assumptions 121 5.1 Introduction 121 5.2 Basic theoretical results 122 5.3 Heteroskedasticity 124 5.4 The regression model with autocorrelated errors 138 5.5 The instrumental variables estimator 149 5.6 Chapter summary 164 Exercises 165 Appendix: Asymptotic results for the OLS and instrumental variables estimators 168 Chapter 6 Univariate Time Series Analysis 173 6.1 Introduction 173 6.2 Time series notation 175 6.3 Trends in time series variables 177 6.4 The autocorrelation function 179 6.5 The autoregressive model 181 6.6 Defining stationarity 195 6.7 Modeling volatility 197 6.8 Chapter summary 205 Exercises 207 Appendix: MA and ARMA models 210 Chapter 7 Regression with Time Series Variables 213 7.1 Introduction 213 7.2 Time series regression when X and Yare stationary 214 7.3 Time series regression when Y and X have unit roots 217 7.4 Time series regression when Y and X have unit roots but are NOTcointegrated 227 7.5 Granger causality 227 7.6 Vector autoregressions 233 7.7 Chapter summary 247 Exercises 248 Appendix: The theory of forecasting 251 Chapter 8 Models for Panel Data 255 8.1 Introduction 255 8.2 The pooled model 256 8.3 Individual effects models 256 8.4 Chapter summary 271 Exercises 272 Chapter 9 Qualitative Choice and Limited Dependent Variable Models 277 9.1 Introduction 277 9.2 Qualitative choice models 278 9.3 Limited dependent variable models 296 9.4 Chapter summary 304 Exercises 306 Chapter 10 Bayesian Econometrics 309 10.1 An overview of Bayesian econometrics 309 10.2 The normal linear regression model with natural conjugate prior and a single explanatory variable 315 10.3 Chapter summary 326 Exercises 326 Appendix: Bayesian analysis of the simple regression model with unknown variance 328 Appendix A: Mathematical Basics 333 Appendix B: Probability Basics 338 Appendix C: Basic Concepts in Asymptotic Theory 348 Appendix D: Writing an Empirical Project 353 Tables 359 Table 1. Area under the standard normal distribution Pr(0 ≤ Z ≤ z) 359 Table 2. Area under the Student t distribution for different degrees of freedom (DF), Pr(Z ≥ z) = α 360 Table 3. Percentiles of the chi-square distribution 361 Table 4a. Area under the F-distribution for different degrees of freedom, ν1 and ν2, Pr(Z ≥ z) = 0.05 362 Table 4b. Area under the F-distribution for different degrees of freedom, ν1 and ν2, Pr(Z ≥ z) = 0.01 363 Bibliography 364 Index 365

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