Econometrics and economic statistics Books

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 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 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 SynopsisTable of Contents1. The Nature of Econometrics and Economic Data. Part I: REGRESSION ANALYSIS WITH CROSS-SECTIONAL DATA. 2. The Simple Regression Model. 3. Multiple Regression Analysis: Estimation. 4. Multiple Regression Analysis: Inference. 5. Multiple Regression Analysis: OLS Asymptotics. 6. Multiple Regression Analysis: Further Issues. 7. Multiple Regression Analysis with Qualitative Information. 8. Heteroskedasticity. 9. More on Specification and Data Problems. Part II: REGRESSION ANALYSIS WITH TIME SERIES DATA. 10. Basic Regression Analysis with Time Series Data. 11. Further Issues in Using OLS with Time Series Data. 12. Serial Correlation and Heteroskedasticity in Time Series Regressions. Part III: ADVANCED TOPICS. 13. Pooling Cross Sections Across Time: Simple Panel Data Methods. 14. Advanced Panel Data Methods. 15. Instrumental Variables Estimation and Two Stage Least Squares. 16. Simultaneous Equations Models. 17. Limited Dependent Variable Models and Sample Selection Corrections. 18. Advanced Time Series Topics. 19. Carrying Out an Empirical Project. Math Refresher A: Basic Mathematical Tools. Math Refresher B: Fundamentals of Probability. Math Refresher C: Fundamentals of Mathematical Statistics. Math Refresher D: Summary of Matrix Algebra. Math Refresher E: The Linear Regression Model in Matrix Form. Answers to Exploring Further Chapter Exercises. Statistical Tables. References. Glossary. Index.

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Book SynopsisThis title has been adapted for use in Europe, the Middle East and Africa and has been tailored to meet the demands of today's lecturers and students.Jeffrey M. Wooldridge's Introduction to Econometrics shows how econometrics is a useful tool for answering questions in business, policy evaluation and forecasting environments. Packed with timely, relevant applications, the text incorporates close to 100 intriguing data sets, available in six formats, with appendices and questions available online.Unique organization pioneered by the author clearly presents applications for today's students. This comprehensive econometrics text pioneered the approach of explicitly covering cross-sectional applications first, followed by time series applications, and, ultimately, panel data applications in the advanced chapters.Practical application prepares students to use econometrics in business today. This unique, comprehensive text applies econometrics to actual real business problems, demonstrating Table of Contents1. The Nature of Econometrics and Economic Data 2. The Simple Regression Model 3. Multiple Regression Analysis: Estimation 4. Multiple Regression Analysis: Inference 5. Multiple Regression Analysis: OLS Asymptotics 6. Multiple Regression Analysis: Further Issues 7. Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables 8. Heteroskedasticity 9. More on Specification and Data Issues 10. Basic Regression Analysis with Time Series Data 11. Further Issues in Using OLS with Time Series Data 12. Serial Correlation and Heteroskedasticity 13. Pooling Cross Sections Across Time: Simple Panel Data Methods 14. Advanced Panel Data Methods 15. Instrumental Variables Estimation and Two Stage Least Squares 16. Simultaneous Equations Models 17. Limited Dependant Variable Models and Sample Selection Corrections 18. Advanced Time Series Topics 19. Carrying Out an Empirical Project

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Book SynopsisAn accessible and contemporary introduction to the methods for determining cause and effect in the social sciencesTrade Review“A new guide to methods for determining cause and effect in the social sciences. In summarising, systematising and prioritising methodological tools for researchers, this book will be of use to all social scientists looking to validate their quantitative findings.”—Dr Simeon Mitropolitski, LSE Review of Books "Cunningham's brilliant book is that rare statistical treatise written for students and practitioners alike. Engaging language and vivid examples bring the tools of causal inference to a broad audience. Read the book, absorb its lessons, and you'll develop the skills you need to credibly assess whether a statistics class, a public policy, or a new business practice truly makes a difference."–Justin Wolfers, University of Michigan "Accessible and engaging. An excellent introduction to the statistics of causal inference."–Alberto Abadie, MIT “Learning about causal effects is the main goal of most empirical research in economics. In this engaging book, Scott Cunningham provides an accessible introduction to this area, full of wisdom and wit and with detailed coding examples for practitioners.”--Guido Imbens, coauthor of Causal Inference "This book will probably shock economics instructors with the clarity, insights, and tools that modern graphical models introduce to the teaching of econometrics. The benefits will outlast the shock."--Judea Pearl, University of California, Los Angeles “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC)

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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 SynopsisA New York Times bestseller "Brilliant, funny…the best math teacher you never had." —San Francisco ChronicleTrade Review"Sparkling and intensely readable…A riff on basic statistics that is neither textbook nor essay but a happy amalgam of the two." -- New York Times"Naked Statistics is an apt title. Charles Wheelan strips away the superfluous outer garments and exposes the underlying beauty of the subject in a way that everyone can appreciate." -- Hal Varian, chief economist at Google"[Wheelan] does something unique here: he makes statistics interesting and fun. His book strips the subject of its complexity to expose the sexy stuff underneath." -- The Economist"Almost anyone interested in sports, politics, business, and the myriad of other areas in which statistics rule the roost today will benefit from this highly readable, on target, and important book." -- Frank Newport, Gallup editor-in-chief"A fun, engaging book that shows why statistics is a vital tool for anyone who wants to understand the modern world." -- Jacob J. Goldstein, NPR’s Planet Money"Two phrases you don’t often see together: ‘statistics primer’ and ‘rollicking good time.’ Until Charlie Wheelan got to it, that is. This book explains the way statistical ideas can help you understand much of everyday life." -- Austan Goolsbee, professor of economics at the University of Chicago and former chairman of the Council of Economic Advisers"A well written, surprisingly funny, and enthusiastic primer on statistics…It is hard to imagine a more accessible introduction to a field with an undeserved reputation for inaccessibility." -- New Republic"With humor and an engaging conversational style, [Wheelan] walks the reader through the basics of statistical concepts and their applications, using real-world examples to illustrate how statistics work and why they matter. All in all, it’s an excellent book." -- Science News"Naked Statistics is the book that I wish I had in 1991, the year that I took stats during my first semester at grad school…Wheelan is a master of explaining the core concepts and methods of statistics in a way that is both accessible and relevant. He is clearly a master teacher, and his gifts are in abundant display in Naked Statistics." -- Inside Higher Ed

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Book SynopsisCharles D. Kirkpatrick II, CMT, relative to technical analysis, is or has been: President, Kirkpatrick & Company, Inc., Kittery, Maine--a private firm specializing in technical research; editor and publisher of the Market Strategist newsletter. Author of several other books on aspects of technical analysis in the trading markets. Adjunct professor of finance, Brandeis University International School of Business, Waltham, Massachusetts. Director and vice president, Market Technicians Association Educational Foundation, Cambridge, Massachusetts--a charitable foundation dedicated to encouraging and providing educational courses in technical analysis at the college and university level. Editor, Journal of Technical Analysis, New York, New York--the official journal of technical analysis research. Director, Market Technicians Association, New York, New York--an association of professional technical analysts. Table of ContentsPart I: Introduction Chapter 1: Introduction to Technical Analysis 1 Chapter 2: The Basic Principle of Technical Analysis--The Trend 7 Chapter 3: History of Technical Analysis 21 Chapter 4: The Technical Analysis Controversy 33 Part II: Markets and Market Indicators Chapter 5: An Overview of Markets 57 Chapter 6: Dow Theory 77 Chapter 7: Sentiment 91 Chapter 8: Measuring Market Strength 143 Chapter 9: Temporal Patterns and Cycles 177 Chapter 10: Flow of Funds 195 Part III: Trend Analysis Chapter 11: History and Construction of Charts 219 Chapter 12: Trends--The Basics 249 Chapter 13: Breakouts, Stops, and Retracements 281 Chapter 14: Moving Averages 305 Part IV: Chart Pattern Analysis Chapter 15: Bar Chart Patterns 333 Chapter 16: Point and Figure Chart Patterns 367 Chapter 17: Short-Term Patterns 393 Part V: Trend Confirmation Chapter 18: Confirmation 439 Part VI: Other Technical Methods and Rules Chapter 19: Cycles 481 Chapter 20: Elliott, Fibonacci, and Gann 509 Part VII: Selection Chapter 21: Selection of Markets and Issues: Trading and Investing 533 Part VIII: System Testing and Management Chapter 22: System Design and Testing 559 Chapter 23: Money and Portfolio Risk Management 589 Part IX: Appendices Appendix A: Basic Statistics 611 Appendix B: Types of Orders and Other Trader Terminology 639 Bibliography 643 Index 675

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Book SynopsisMaster the fundamental concepts and applications of operations (OM) and supply chain management (SCM) with OPERATIONS AND SUPPLY CHAIN MANAGEMENT, 3E by award-winning authors Collier/Evans. This edition balances coverage of both manufacturing and service businesses with the latest updates, an additional new SCM chapter and new discussions that highlight the latest changes in OM and SCM. Clear explanations are supported with contemporary examples and new and updated case studies that demonstrate how concepts apply. Discussions highlight new techniques and principles as well as the most recent Excel techniques and digital tools. Solved problems further guide you through key formulas and computations. MindTap online learning platform is available for both manual calculations and the use of Excel spreadsheet templates and models. MindTap's algorithmic homework and interactive learning tools also show you how to apply qualitative and quantitative reasoning to today's OM and SCM concepts.Table of ContentsPART 1: BASIC CONCEPTS OF OM AND VALUE CHAINS. 1. Operations Management and Value Chains. 2. Analytics and Performance Measurement in Operations and Value Chains. 3. Operations Strategy. 4. Technology and Operations Management. PART 2: DESIGNING OPERATIONS AND SUPPLY CHAINS 5. Goods and Service Design. 6. Supply Chain Design. 7. Process Selection, Design, and Improvement. 8. Facility and Work Design. PART 3: MANAGING OPERATIONS AND SUPPLY CHAINS. 9. Forecasting and Demand Planning. 10. Capacity Management. 11. Process Analysis and Resource Utilization. 12. Managing Inventories in Supply Chains. 13. Supply Chain Management and Logistics. 14. Resource Management. 15. Operations Scheduling and Sequencing. 16. Quality Management. 17. Quality Control & SPC. 18. Lean Operating Systems. 19. Project Management. 20. Building Resilience and Continuity in Operations and Supply Chains Supplement A: Probability and Statistics. Supplement B: Decision Analysis. Supplement C: Break-Even Analysis. Supplement D: Linear Optimization. Supplement E: The Transportation and Assignment Problems. Supplement F: Queuing Models. Supplement G: Simulation. Appendix A: Areas for the Cumulative Standard Normal Distribution. Appendix B: Factors for Control Charts. Appendix C: Integrative Case: Diamond Global Supply Chain ��� Hudson Jewelers Endnotes. Glossary. Index.

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Book SynopsisContains Nearly 100 Pages of New MaterialThe recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modeling, Second Edition presents updates on model developments that have occurred since the publication of the best-selling first edition.New to the Second Edition An expanded section on techniques for the generation of loss distributions Introductory sections on new topics, such as spectral risk measures, an axiomatic approach to capital allocation, and nonhomogeneous Markov chains Updated sections on the probability of default, exposure-at-default, loss-given-default, and regulatory capital A new section on multi-period models Recent devel

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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 SynopsisA graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.Trade Review"A carefully prepared and well written book... Without doubt, it can be recommended as a very valuable encyclopedia and textbook for a reader who is looking for a mainly theoretical textbook which combines traditional time series analysis with a review of recent research areas."--Journal of EconomicsTable of ContentsPreface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman Filter37214Generalized Method of Moments40915Models of Nonstationary Time Series43516Processes with Deterministic Time Trends45417Univariate Processes with Unit Roots47518Unit Roots in Multivariate Time Series54419Cointegration57120Full-Information Maximum Likelihood Analysis of Cointegrated Systems63021Time Series Models of Heteroskedasticity65722Modeling Time Series with Changes in Regime677A Mathematical Review704B Statistical Tables751C Answers to Selected Exercises769D Greek Letters and Mathematical Symbols Used in the Text786Author Index789Subject Index792

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Book SynopsisTrade Review“Mercilessly exposes the downside of the cult of measurement and managerialism.”—The Economist“Muller delivers a riposte to bean counters everywhere with this trenchant study of our fixation with performance metrics.”—Barbara Kiser, Nature “Highly readable.”—Luke Johnson, Sunday Times“Many of us have the vague sense that metrics are leading us astray, stripping away context, devaluing subtle human judgment, and rewarding those who know how to play the system. Muller’s book crisply explains where this fashion came from, why it can be so counterproductive and why we don’t learn. It should be required reading for any manager on the verge of making the Vietnam body count mistake all over again.”—Tim Harford, Financial Times

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Book SynopsisYou don't have to be a mathematician to maximize the power of quantitative methods. Written for the current-or future-business professional, QUANTITATIVE METHODS FOR BUSINESS, 13E makes it easy for you to understand how you can most effectively use quantitative methods to make smart, successful decisions. The book's hallmark problem-scenario approach guides you step by step through the application of mathematical concepts and techniques. Memorable real-life examples demonstrate how and when to use the methods found in the book, while instant online access provides you with Excel worksheets, LINGO, and the Excel add-in Analytic Solver Platform. The chapter on simulation includes a more elaborate treatment of uncertainty by using Microsoft Excel to develop spreadsheet simulation models. The new edition also includes a more holistic approach to variability in project management. Completely up to date, QUANTITATIVE METHODS FOR BUSINESS, 13E reflects the latest trends, issues, and practicesTable of ContentsPreface. 1. Introduction. 2. Introduction to Probability. 3. Probability Distributions. 4. Decision Analysis. 5. Utility and Game Theory. 6. Time Series Analysis and Forecasting. 7. Introduction to Linear Programming. 8. Linear Programming: Sensitivity Analysis and Interpretation of Solution. 9. Linear Programming Applications in Marketing, Finance, and Operations Management. 10. Distribution and Network Models. 11. Integer Linear Programming. 12. Advanced Optimization Applications. 13. Project Scheduling: PERT/CPM. 14. Inventory Models. 15. Waiting Line Models. 16. Simulation. 17. Markov Processes. Appendix A: Building Spreadsheet Models. Appendix B: Binomial Probabilities. Appendix C: Poisson Probabilities. Appendix D: Areas for the Standard Normal Distribution. Appendix E: Values for e-��. Appendix F: References and Bibliography. Appendix G: Self-Test Solutions and Answers to Even-Numbered Problems.

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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 SynopsisTailored to the needs of applied econometricians and social scientists, this work emphasizes nonparametric techniques suited to the rich array of data types - continuous, nominal, and ordinal - within one coherent framework. It also covers the various material necessary to understand and apply nonparametric methods for real-world problems.Trade Review"Overall, the text is a must for graduate students undertaking research in this area; the large number of exercises at the end of each chapter makes it very suitable for a graduate class on nonparametric and semiparametric techniques. In addition, because the coverage of the book is very comprehensive and up-to-date, it constitutes an excellent reference for researchers applying these techniques. Therefore, it can satisfy the needs of both audiences with a solid background in theoretical econometrics and more applied audiences."--Margarita Genius, European Review of Agricultural Economics "This book is ideal for a specialised graduate course. Li and Racine have done a fantastic job of bringing together all the latest developments in non-parametric estimation and treating them in a unified, accessible way. In particular, recent developments on using mixed continuous and discrete data, research to which Li and Raci have contributed immensely, are well covered."--Economic RecordTable of ContentsPreface xvii PART I: Nonparametric Kernel Methods 1 Chapter 1: Density Estimation 3 1.1 Univariate Density Estimation 4 1.2 Univariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 14 1.3 Univariate Bandwidth Selection: Cross-Validation ZMethods 15 1.3.1 Least Squares Cross-Validation 15 1.3.2 Likelihood Cross-Validation 18 1.3.3 An Illustration of Data-Driven Bandwidth Selection 19 1.4 Univariate CDF Estimation 19 1.5 Univariate CDF Bandwidth Selection: Cross- Validation Methods 23 1.6 Multivariate Density Estimation 24 1.7 Multivariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 26 1.8 Multivariate Bandwidth Selection: Cross-Validation Methods 27 1.8.1 Least Squares Cross-Validation 27 1.8.2 Likelihood Cross-Validation 28 1.9 Asymptotic Normality of Density Estimators 28 1.10 Uniform Rates of Convergence 30 1.11 Higher Order Kernel Functions 33 1.12 Proof of Theorem 1.4 (Uniform Almost Sure Convergence) 35 1.13 Applications 40 1.13.1 Female Wage Inequality 41 1.13.2 Unemployment Rates and City Size 43 1.13.3 Adolescent Growth 44 1.13.4 Old Faithful Geyser Data 44 1.13.5 Evolution of Real Income Distribution in Italy, 1951-1998 45 1.14 Exercises 47 Chapter 2: Regression 57 2.1 Local Constant Kernel Estimation 60 2.1.1 Intuition Underlying the Local Constant Kernel Estimator 64 2.2 Local Constant Bandwidth Selection 66 2.2.1 Rule-of-Thumb and Plug-In Methods 66 2.2.2 Least Squares Cross-Validation 69 2.2.3 AICc 72 2.2.4 The Presence of Irrelevant Regressors 73 2.2.5 Some Further Results on Cross-Validation 78 2.3 Uniform Rates of Convergence 78 2.4 Local Linear Kernel Estimation 79 2.4.1 Local Linear Bandwidth Selection: Least Squares Cross-Validation 83 2.5 Local Polynomial Regression (General pth Order) 85 2.5.1 The Univariate Case 85 2.5.2 The Multivariate Case 88 2.5.3 Asymptotic Normality of Local Polynomial Estimators 89 2.6 Applications 92 2.6.1 Prestige Data 92 2.6.2 Adolescent Growth 92 2.6.3 Inflation Forecasting and Money Growth 93 2.7 Proofs 97 2.7.1 Derivation of (2.24) 98 2.7.2 Proof of Theorem 2.7 100 2.7.3 Definitions of Al,p+1 and Vl Used in Theorem 2.10 106 2.8 Exercises 108 Chapter 3: Frequency Estimation with Mixed Data 115 3.1 Probability Function Estimation with Discrete Data 116 3.2 Regression with Discrete Regressors 118 3.3 Estimation with Mixed Data: The Frequency Approach 118 3.3.1 Density Estimation with Mixed Data 118 3.3.2 Regression with Mixed Data 119 3.4 Some Cautionary Remarks on Frequency Methods 120 3.5 Proofs 122 3.5.1 Proof of Theorem 3.1 122 3.6 Exercises 123 Chapter 4: Kernel Estimation with Mixed Data 125 4.1 Smooth Estimation of Joint Distributions with Discrete Data 126 4.2 Smooth Regression with Discrete Data 131 4.3 Kernel Regression with Discrete Regressors: The Irrelevant Regressor Case 134 4.4 Regression with Mixed Data: Relevant Regressors 136 4.4.1 Smooth Estimation with Mixed Data 136 4.4.2 The Cross-Validation Method 138 4.5 Regression with Mixed Data: Irrelevant Regressors 140 4.5.1 Ordered Discrete Variables 144 4.6 Applications 145 4.6.1 Food-Away-from-Home Expenditure 145 4.6.2 Modeling Strike Volume 147 4.7 Exercises 150 Chapter 5: Conditional Density Estimation 155 5.1 Conditional Density Estimation: Relevant Variables 155 5.2 Conditional Density Bandwidth Selection 157 5.2.1 Least Squares Cross-Validation: Relevant Variables 157 5.2.2 Maximum Likelihood Cross-Validation: Relevant Variables 160 5.3 Conditional Density Estimation: Irrelevant Variables 162 5.4 The Multivariate Dependent Variables Case 164 5.4.1 The General Categorical Data Case 167 5.4.2 Proof of Theorem 5.5 168 5.5 Applications 171 5.5.1 A Nonparametric Analysis of Corruption 171 5.5.2 Extramarital Affairs Data 172 5.5.3 Married Female Labor Force Participation 175 5.5.4 Labor Productivity 177 5.5.5 Multivariate Y Conditional Density Example: GDP Growth and Population Growth Conditional on OECD Status 178 5.6 Exercises 180 Chapter 6: Conditional CDF and Quantile Estimation 181 6.1 Estimating a Conditional CDF with Continuous Covariates without Smoothing the Dependent Variable 182 6.2 Estimating a Conditional CDF with Continuous Covariates Smoothing the Dependent Variable 184 6.3 Nonparametric Estimation of Conditional Quantile Functions 189 6.4 The Check Function Approach 191 6.5 Conditional CDF and Quantile Estimation with Mixed Discrete and Continuous Covariates 193 6.6 A Small Monte Carlo Simulation Study 196 6.7 Nonparametric Estimation of Hazard Functions 198 6.8 Applications 200 6.8.1 Boston Housing Data 200 6.8.2 Adolescent Growth Charts 202 6.8.3 Conditional Value at Risk 202 6.8.4 Real Income in Italy, 1951-1998 206 6.8.5 Multivariate Y Conditional CDF Example: GDP Growth and Population Growth Conditional on OECD Status 206 6.9 Proofs 209 6.9.1 Proofs of Theorems 6.1, 6.2, and 6.4 209 6.9.2 Proofs of Theorems 6.5 and 6.6 (Mixed Covariates Case) 214 6.10 Exercises 215 PART II: Semiparametric Methods 219 Chapter 7: Semiparametric Partially Linear Models 221 7.1 Partially Linear Models 222 7.1.1 Identification of 222 7.2 Robinson's Estimator 222 7.2.1 Estimation of the Nonparametric Component 228 7.3 Andrews's MINPIN Method 230 7.4 Semiparametric Efficiency Bounds 233 7.4.1 The Conditionally Homoskedastic Error Case 233 7.4.2 The Conditionally Heteroskedastic Error Case 235 7.5 Proofs 238 7.5.1 Proof of Theorem 7.2 238 7.5.2 Verifying Theorem 7.3 for a Partially Linear Model 244 7.6 Exercises 246 Chapter 8: Semiparametric Single Index Models 249 8.1 Identification Conditions 251 8.2 Estimation 253 8.2.1 Ichimura's Method 253 8.3 Direct Semiparametric Estimators for 258 8.3.1 Average Derivative Estimators 258 8.3.2 Estimation of g() 262 8.4 Bandwidth Selection 263 8.4.1 Bandwidth Selection for Ichimura's Method 263 8.4.2 Bandwidth Selection with Direct Estimation Methods 265 8.5 Klein and Spady's Estimator 266 8.6 Lewbel's Estimator 267 8.7 Manski's Maximum Score Estimator 269 8.8 Horowitz's Smoothed Maximum Score Estimator 270 8.9 Han's Maximum Rank Estimator 270 8.10 Multinomial Discrete Choice Models 271 8.11 Ai's Semiparametric Maximum Likelihood Approach 272 8.12 A Sketch of the Proof of Theorem 8.1 275 8.13 Applications 277 8.13.1 Modeling Response to Direct Marketing Catalog Mailings 277 8.14 Exercises 281 Chapter 9: Additive and Smooth (Varying) Coefficient Semiparametric Models 283 9.1 An Additive Model 283 9.1.1 The Marginal Integration Method 284 9.1.2 A Computationally Efficient Oracle Estimator 286 9.1.3 The Ordinary Backfitting Method 289 9.1.4 The Smoothed Backfitting Method 290 9.1.5 Additive Models with Link Functions 295 9.2 An Additive Partially Linear Model 297 9.2.1 A Simple Two-Step Method 299 9.3 A Semiparametric Varying (Smooth) Coefficient Model 301 9.3.1 A Local Constant Estimator of the Smooth Coefficient Function 302 9.3.2 A Local Linear Estimator of the Smooth Coefficient Function 303 9.3.3 Testing for a Parametric Smooth Coefficient Model 306 9.3.4 Partially Linear Smooth Coefficient Models 308 9.3.5 Proof of Theorem 9.3 310 9.4 Exercises 312 Chapter 10: Selectivity Models 315 10.1 Semiparametric Type-2 Tobit Models 316 10.2 Estimation of a Semiparametric Type-2 Tobit Model 317 10.2.1 Gallant and Nychka's Estimator 318 10.2.2 Estimation of the Intercept in Selection Models 319 10.3 Semiparametric Type-3 Tobit Models 320 10.3.1 Econometric Preliminaries 320 10.3.2 Alternative Estimation Methods 323 10.4 Das, Newey and Vella's Nonparametric Selection Model 328 10.5 Exercises 330 Chapter 11: Censored Models 331 11.1 Parametric Censored Models 332 11.2 Semiparametric Censored Regression Models 334 11.3 Semiparametric Censored Regression Models with Nonparametric Heteroskedasticity 336 11.4 The Univariate Kaplan-Meier CDF Estimator 338 11.5 The Multivariate Kaplan-Meier CDF Estimator 341 11.5.1 Nonparametric Regression Models with Random Censoring 343 11.6 Nonparametric Censored Regression 345 11.6.1 Lewbel and Linton's Approach 345 11.6.2 Chen, Dahl and Khan's Approach 346 11.7 Exercises 348 III Consistent Model Specification Tests 349 Chapter 12: Model Specification Tests 351 12.1 A Simple Consistent Test for Parametric Regression Functional Form 354 12.1.1 A Consistent Test for Correct Parametric Functional Form 355 12.1.2 Mixed Data 360 12.2 Testing for Equality of PDFs 362 12.3 More Tests Related to Regression Functions 365 12.3.1 Hardle and Mammen's Test for a Parametric Regression Model 365 12.3.2 An Adaptive and Rate Optimal Test 367 12.3.3 A Test for a Parametric Single Index Model 369 12.3.4 A Nonparametric Omitted Variables Test 370 12.3.5 Testing the Significance of Categorical Variables 375 12.4 Tests Related to PDFs 378 12.4.1 Testing Independence between Two Random Variables 378 12.4.2 A Test for a Parametric PDF 380 12.4.3 A Kernel Test for Conditional Parametric Distributions 382 12.5 Applications 385 12.5.1 Growth Convergence Clubs 385 12.6 Proofs 388 12.6.1 Proof of Theorem 12.1 388 12.6.2 Proof of Theorem 12.2 389 12.6.3 Proof of Theorem 12.5 389 12.6.4 Proof of Theorem 12.9 391 12.7 Exercises 394 Chapter 13: Nonsmoothing Tests 397 13.1 Testing for Parametric Regression Functional Form 398 13.2 Testing for Equality of PDFs 401 13.3 A Nonparametric Significance Test 401 13.4 Andrews's Test for Conditional CDFs 402 13.5 Hong's Tests for Serial Dependence 404 13.6 More on Nonsmoothing Tests 408 13.7 Proofs 409 13.7.1 Proof of Theorem 13.1 409 13.8 Exercises 410 PART IV: Nonparametric Nearest Neighbor and Series Methods 413 Chapter 14: K-Nearest Neighbor Methods 415 14.1 Density Estimation: The Univariate Case 415 14.2 Regression Function Estimation 419 14.3 A Local Linear k-nn Estimator 421 14.4 Cross-Validation with Local Constant k-nn Estimation 422 14.5 Cross-Validation with Local Linear k-nn Estimation 425 14.6 Estimation of Semiparametric Models with k-nn Methods 427 14.7 Model Specification Tests with k-nn Methods 428 14.7.1 A Bootstrap Test 431 14.8 Using Different k for Different Components of x 432 14.9 Proofs 432 14.9.1 Proof of Theorem 14.1 435 14.9.2 Proof of Theorem 14.5 435 14.9.3 Proof of Theorem 14.10 440 14.10 Exercises 444 Chapter 15: Nonparametric Series Methods 445 15.1 Estimating Regression Functions 446 15.1.1 Convergence Rates 449 15.2 Selection of the Series Term K 451 15.2.1 Asymptotic Normality 453 15.3 A Partially Linear Model 454 15.3.1 An Additive Partially Linear Model 455 15.3.2 Selection of Nonlinear Additive Components 461 15.3.3 Estimating an Additive Model with a Known Link Function 463 15.4 Estimation of Partially Linear Varying Coefficient Models 466 15.4.1 Testing for Correct Parametric Regression Functional Form 471 15.4.2 A Consistent Test for an Additive Partially Linear Model 474 15.5 Other Series-Based Tests 479 15.6 Proofs 480 15.6.1 Proof of Theorem 15.1 480 15.6.2 Proof of Theorem 15.3 484 15.6.3 Proof of Theorem 15.6 488 15.6.4 Proof of Theorem 15.9 492 15.6.5 Proof of Theorem 15.10 497 15.7 Exercises 502 PART V: Time Series, Simultaneous Equation, and Panel Data Models 503 Chapter 16: Instrumental Variables and Efficient Estimation of Semiparametric Models 505 16.1 A Partially Linear Model with Endogenous Regressors in the Parametric Part 505 16.2 A Varying Coefficient Model with Endogenous Regressors in the Parametric Part 509 16.3 Ai and Chen's Efficient Estimator with Conditional Moment Restrictions 511 16.3.1 Estimation Procedures 511 16.3.2 Asymptotic Normality for 513 16.3.3 A Partially Linear Model with the Endogenous Regressors in the Nonparametric Part 515 16.4 Proof of Equation (16.16) 517 16.5 Exercises 520 Chapter 17: Endogeneity in Nonparametric Regression Models 521 17.1 A Nonparametric Model 521 17.2 A Triangular Simultaneous Equation Model 522 17.3 Newey-Powell Series-Based Estimator 527 17.4 Hall and Horowitz's Kernel-Based Estimator 529 17.5 Darolles, Florens and Renault's Estimator 532 17.6 Exercises 533 Chapter 18: Weakly Dependent Data 535 18.1 Density Estimation with Dependent Data 537 18.1.1 Uniform Almost Sure Rate of Convergence 541 18.2 Regression Models with Dependent Data 541 18.2.1 The Martingale Difference Error Case 541 18.2.2 The Autocorrelated Error Case 544 18.2.3 One-Step-Ahead Forecasting 546 18.2.4 d-Step-Ahead Forecasting 547 18.2.5 Estimation of Nonparametric Impulse Response Functions 548 18.3 Semiparametric Models with Dependent Data 551 18.3.1 A Partially Linear Model with Dependent Data 551 18.3.2 Additive Regression Models 552 18.3.3 Varying Coefficient Models with Dependent Data 553 18.4 Testing for Serial Correlation in Semiparametric Models 554 18.4.1 The Test Statistic and Its Asymptotic Distribution 554 18.4.2 Testing Zero First Order Serial Correlation 555 18.5 Model Specification Tests with Dependent Data 556 18.5.1 A Kernel Test for Correct Parametric Regression Functional Form 556 18.5.2 Nonparametric Significance Tests 557 18.6 Nonsmoothing Tests for Regression Functional Form 558 18.7 Testing Parametric Predictive Models 559 18.7.1 In-Sample Testing of Conditional CDFs 559 18.7.2 Out-of-Sample Testing of Conditional CDFs 562 18.8 Applications 564 18.8.1 Forecasting Short-Term Interest Rates 564 18.9 Nonparametric Estimation with Nonstationary Data 566 18.10 Proofs 567 18.10.1 Proof of Equation (18.9) 567 18.10.2 Proof of Theorem 18.2 569 18.11 Exercises 572 Chapter 19: Panel Data Models 575 19.1 Nonparametric Estimation of Panel Data Models: Ignoring the Variance Structure 576 19.2 Wang's Efficient Nonparametric Panel Data Estimator 578 19.3 A Partially Linear Model with Random Effects 584 19.4 Nonparametric Panel Data Models with Fixed Effects 586 19.4.1 Error Variance Structure Is Known 587 19.4.2 The Error Variance Structure Is Unknown 590 19.5 A Partially Linear Model with Fixed Effects 592 19.6 Semiparametric Instrumental Variable Estimators 594 19.6.1 An Infeasible Estimator 594 19.6.2 The Choice of Instruments 595 19.6.3 A Feasible Estimator 597 19.7 Testing for Serial Correlation and for Individual Effects in Semiparametric Models 599 19.8 Series Estimation of Panel Data Models 602 19.8.1 Additive Effects 602 19.8.2 Alternative Formulation of Fixed Effects 604 19.9 Nonlinear Panel Data Models 606 19.9.1 Censored Panel Data Models 607 19.9.2 Discrete Choice Panel Data Models 614 19.10 Proofs 618 19.10.1 Proof of Theorem 19.1 618 19.10.2 Leading MSE Calculation of Wang's Estimator 621 19.11 Exercises 624 Chapter 20: Topics in Applied Nonparametric Estimation 627 20.1 Nonparametric Methods in Continuous-Time Models 627 20.1.1 Nonparametric Estimation of Continuous-Time Models 627 20.1.2 Nonparametric Tests for Continuous-Time Models 632 20.1.3 Ait-Sahalia's Test 632 20.1.4 Hong and Li's Test 633 20.1.5 Proofs 636 20.2 Nonparametric Estimation of Average Treatment Effects 639 20.2.1 The Model 640 20.2.2 An Application: Assessing the Efficacy of Right Heart Catheterization 642 20.3 Nonparametric Estimation of Auction Models 645 20.3.1 Estimation of First Price Auction Models 645 20.3.2 Conditionally Independent Private Information Auctions 648 20.4 Copula-Based Semiparametric Estimation of Multivariate Distributions 651 20.4.1 Some Background on Copula Functions 651 20.4.2 Semiparametric Copula-Based Multivariate Distributions 652 20.4.3 A Two-Step Estimation Procedure 653 20.4.4 A One-Step Efficient Estimation Procedure 655 20.4.5 Testing Parametric Functional Forms of a Copula 657 20.5 A Semiparametric Transformation Model 659 20.6 Exercises 662 A Background Statistical Concepts 663 1.1 Probability, Measure, and Measurable Space 663 1.2 Metric, Norm, and Functional Spaces 672 1.3 Limits and Modes of Convergence 680 1.3.1 Limit Supremum and Limit Infimum 680 1.3.2 Modes of Convergence 681 1.4 Inequalities, Laws of Large Numbers, and Central Limit Theorems 688 1.5 Exercises 694 Bibliography 697 Author Index 737 Subject Index 744

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Book SynopsisAre you innately curious about dynamically inter-operating financial markets? Since the crisis of 2008, there is a need for professionals with more understanding about statistics and data analysis, who can discuss the various risk metrics, particularly those involving extreme events. By providing a resource for training students and professionals in basic and sophisticated analytics, this book meets that need. It offers both the intuition and basic vocabulary as a step towards the financial, statistical, and algorithmic knowledge required to resolve the industry problems, and it depicts a systematic way of developing analytical programs for finance in the statistical language R. Build a hands-on laboratory and run many simulations. Explore the analytical fringes of investments and risk management. Bennett and Hugen help profit-seeking investors and data science students sharpen their skills in many areas, including time-series, forecasting, portfolio selection, covariance clustering, pTrade Review'A very well-written text on financial analytics, focusing on developing statistical models and using simulation to better understand financial data. R is used throughout for examples, allowing the reader to use the text and code to actively engage in the financial market. It is simply the best text on this subject that I have seen. Highly recommended.' Joseph M. Hilbe, Arizona State University'There's a new source in town for those who want to learn R and it's a good, old-fashioned book called Financial Analytics with R: Building a Laptop Laboratory for Data Science … it is a one-stop-shop for everything you need to know to use R for financial analysis. The book meaningfully combines an education on R with relevant problem-solving in financial analysis. [It] is thorough and contextualized with examples from extreme financial events in recent times such as the housing crisis and the Euro crisis. The code samples are relevant - think functions to compute the Sharpe ratio or to implement Bayesian reasoning - and answer many of the questions you might have while trying them out. This is a book that will make you a better practitioner/student/analyst/entrepreneur - whatever your goals may be.' Carrie Shaw, Quandl'The book at hand is unusual in addressing beginners, and in treating R as a general number crunching tool. … It is also one of very few books on R really written for non-statistician non-programmers. … R seems a viable programming language for STEM students to learn, and learning a programming language seems a good idea for such students. This book appears to be the best option for accomplishing that.' Robert W. Hayden, Mathematical Association of America Reviews (www.maa.org)Table of ContentsPreface; Acknowledgements; 1. Analytical thinking; 2. The R language for statistical computing; 3. Financial statistics; 4. Financial securities; 5. Dataset analytics and risk measurement; 6. Time series analysis; 7. The Sharpe ratio; 8. Markowitz mean-variance optimization; 9. Cluster analysis; 10. Gauging the market sentiment; 11. Simulating trading strategies; 12. Data mining using fundamentals; 13. Prediction using fundamentals; 14. Binomial model for options; 15. Black–Scholes model and option implied volatility; Appendix. Probability distributions and statistical analysis; Index.

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Book SynopsisThis book deals with methods and models of microeconometrics, the statistical modeling of behavioral relationships based on data from sample surveys or actual or quasi-social experiments. The book is oriented to the graduate student and researcher using such data. The level of the book is post-first year PhD economics.Trade Review'This book presents an elegant and accessible treatment of the broad range of rapidly expanding topics currently being studied by microeconometricians. Thoughtful, intuitive, and careful in laying out central concepts of sophisticated econometric methodologies, it is not only an excellent textbook for students, but also an invaluable reference text for practitioners and researchers.' Cheng Hsiao, University of Southern California'I wish Microeconometrics was available when I was a student! Here, in one place - and in clear and readable prose - you can find all of the tools that are necessary to do cutting-edge applied economic analysis, and with many helpful examples.' Alan Krueger, Princeton University'Cameron and Trivedi have written a remarkably thorough and up-to-date treatment of microeconometric methods. This is not a superficial cookbook; the early chapters carefully lay the theoretical foundations on which the authors build their discussion of methods for discrete and limited dependent variables and for analysis of longitudinal data. A distinctive feature of the book is its attention to cutting-edge topics like semiparametric regression, bootstrap methods, simulation-based estimation, and empirical likelihood estimation. A highly valuable book.' Gary Solon, University of Michigan'The empirical analysis of micro data is more widespread than ever before. The book by Cameron and Trivedi contains a superb treatment of all the methods that economists like to apply to such data. What is more, it fully integrates a number of exciting new methods that have become applicable due to recent advances in computer technology. The text is in perfect balance between econometric theory and empirical intuition, and it contains many insightful examples.' Gerard J. van den Berg, Free University, Amsterdam, The Netherlands'… it is well organised and well written … the authors are to be congratulated on this sure-footed addition to the econometrics literature.' The Times Higher Education SupplementTable of Contents1. Introduction; 2. Causal and non-causal models; 3. Microeconomic data structures; 4. Linear models; 5. ML and NLS estimation; 6. GMM and systems estimation; 7. Hypothesis tests; 8. Specification tests and model selection; 9. Semiparametric methods; 10. Numerical optimization; 11. Bootstrap methods; 12. Simulation-based methods; 13. Bayesian methods; 14. Binary outcome models; 15. Multinomial models; 16. Tobit and selection models; 17. Transition data: survival analysis; 18. Mixture models and unobserved heterogeneity; 19. Models of multiple hazards; 20. Models of count data; 21. Linear panel models: basics; 22. Linear panel models: extensions; 23. Nonlinear panel models; 24. Stratified and clustered samples; 25. Treatment evaluation; 26. Measurement error models; 27. Missing data and imputation; A. Asymptotic theory; B. Making pseudo-random draw.

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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 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 SynopsisNow in its fourth edition, this comprehensive introduction of fundamental panel data methodologies provides insights on what is most essential in panel literature. A capstone to the forty-year career of a pioneer of panel data analysis, this new edition''s primary contribution will be the coverage of advancements in panel data analysis, a statistical method widely used to analyze two or higher-dimensional panel data. The topics discussed in early editions have been reorganized and streamlined to comprehensively introduce panel econometric methodologies useful for identifying causal relationships among variables, supported by interdisciplinary examples and case studies. This book, to be featured in Cambridge''s Econometric Society Monographs series, has been the leader in the field since the first edition. It is essential reading for researchers, practitioners and graduate students interested in the analysis of microeconomic behavior.Trade ReviewA masterful new edition of Hsiao's classic text on panel data. This is a superbly comprehensive and accessible source for panel data with modern approaches to inference and identification, helpful to econometricians and other quantitative social scientists. Esfandiar Maasoumi, Emery UniversityThe latest edition of Cheng Hsiao's panel data monograph is most welcome to the econometrics profession. Benefitting from Professor Hsiao's deep understanding and insight, it has, since its first edition, not only become required reading for students, researchers and practitioners, but surely deserves no small credit for the huge growth of interest and activity in panel data. In this 4th edition, Professor Hsiao has very successfully built on the foundations of the earlier ones. Peter M. Robinson, London School of EconomicsProfessor Hsiao has done it again. This edition provides a lucid and comprehensive account of often complex problems, ranging from the analysis of panel data models with interactive effects, heterogeneity, spatial dependence, simultaneous dynamic models, to program evaluation – many areas to which he himself has made significant and lasting contributions. I have learned a great deal from the past three editions, and I very much look forward to the fourth edition and strongly recommend it to both students and research scholars of panel data alike. Hashem Pesaran, John Elliot University of Southern CaliforniaCheng Hsiao's Analysis of Panel Data has undoubtedly become the classic text book reference on panel data econometric methods. It is to be recommended for the clarity and deepness of its exposition, its wide coverage of the abundant and rapidly developing specialized literature, and its remarkable capacity to focus on what is most essential in this literature. Jacques Mairesse, Collège de FranceTable of ContentsPreface; 1. Introduction; 2. Static models with additive effects; 3. Dynamic models with additive effects; 4. Static simultaneous models with additive effects; 5. Dynamic system; 6. Qualitative choice models; 7. Limited dependent and sample section models; 8. Some nonlinear models; 9. Miscellaneous topics; 10. Interactive effects models; 11. Spatial models and cross-sectional dependent data; 12. Program evaluation; 13. Varying coefficients models; 14. Big data analysis.

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Book SynopsisThis well-balanced introduction to enterprise risk management integrates quantitative and qualitative approaches and motivates key mathematical and statistical methods with abundant real-world cases - both successes and failures. Worked examples and end-of-chapter exercises support readers in consolidating what they learn. The mathematical level, which is suitable for graduate and senior undergraduate students in quantitative programs, is pitched to give readers a solid understanding of the concepts and principles involved, without diving too deeply into more complex theory. To reveal the connections between different topics, and their relevance to the real world, the presentation has a coherent narrative flow, from risk governance, through risk identification, risk modelling, and risk mitigation, capped off with holistic topics - regulation, behavioural biases, and crisis management - that influence the whole structure of ERM. The result is a text and reference that is ideal for graduate and senior undergraduate students, risk managers in industry, and anyone preparing for ERM actuarial exams.Trade Review'Quantitative Enterprise Risk Management can be strongly recommended to anyone seeking to develop their skills in risk management. The book will be particularly useful for those seeking to master the more challenging technical aspects of risk management missing in other textbooks.' Andrew Cairns, Heriot-Watt University'This hits the sweet spot between overly abstract mathematical and overly 'math lean' presentations of enterprise risk management.' Gary Hatfield, University of Minnesota'Hardy and Saunders have written a masterpiece that not only explains [ERM] from a quantitative perspective, but also manages to bridge the gap between it and more qualitative approaches. It impressively covers the whole spectrum from risk taxonomy, risk modelling and measurement, risk mitigation, risk transfer up to (behavioural) risk, and crisis management. I highly recommend it to all those who want to get a deeper understanding of ERM.' Rudi Zagst, Technical University of MunichTable of ContentsPreface; 1. Introduction to enterprise risk management; 2. Risk taxonomy; 3. Risk measures; 4. Frequency-Severity analysis; 5. Extreme value theory; 6. Copulas; 7. Stress testing; 8. Market risk models; 9. Short term portfolio risk; 10. Economic scenario generators; 11. Interest rate risk; 12. Credit risk; 13. Liquidity risk; 14. Model risk and governance; 15. Risk mitigation using options and derivatives; 16. Risk transfer; 17. Regulation of financial institutions; 18. Risk adjusted measures of profit and capital allocation; 19. Behavioural risk management; 20. Crisis management; A. Probability and statistics review; References; Index.

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Book SynopsisThis new and exciting book offers a fresh approach to quantitative finance and utilises novel features, including stereoscopic images which permit 3D visualisation of complex subjects without the need for additional tools. Offering an integrated approach to the subject, A First Course in Quantitative Finance introduces students to the architecture of complete financial markets before exploring the concepts and models of modern portfolio theory, derivative pricing and fixed income products in both complete and incomplete market settings. Subjects are organised throughout in a way that encourages a gradual and parallel learning process of both the economic concepts and their mathematical descriptions, framed by additional perspectives from classical utility theory, financial economics and behavioural finance. Suitable for postgraduate students studying courses in quantitative finance, financial engineering and financial econometrics as part of an economics, finance, econometric or mathemTrade Review'A First Course in Quantitative Finance is a gentle introduction in a complicated subject. It covers most important topics - such as portfolio optimisation, derivative pricing, and fixed income products - and discusses them from the perspective of financial economics and financial mathematics. It provides the necessary mathematical background, contains the financial discussion, and is full of illustrative examples. It will be useful for anyone who wants to study the subject area on an advanced level.' Rüdiger Kiesel, Universität Duisburg-Essen'This is a remarkably complete book on all aspects of modern finance, covering topics from the puzzles of financial economics, through modern portfolio management to the pricing of exotic options under stochastic volatility at an equally accessible yet state-of-the-art level. Quants, portfolio managers, students and teachers of finance alike will find it to be an invaluable source of insights and a must-have reference to have on their desks.' Peter Tankov, École nationale de la statistique et de l'administration économiqueTable of Contents1. Introduction; Part I. Technical Basics: 2. A primer on probability; 3. Vector spaces; 4. Utility theory; Part II. Financial Markets and Portfolio Theory: 5. Architecture of financial markets; 6. Modern portfolio theory; 7. CAPM and APT; 8. Portfolio performance and management; 9. Financial economics; 10. Behavioral finance; Part III. Derivatives: 11. Forwards, futures and options; 12. The binomial model; 13. The Black–Scholes theory; 14. Exotics in the Black–Scholes model; 15. Deterministic volatility; 16. Stochastic volatility; 17. Processes with jumps; Part IV. The Fixed-Income World: 18. Basic fixed-income instruments; 19. Plain vanilla fixed-income derivatives; 20. Term structure models; 21. The LIBOR market model; Appendix A. Complex analysis; Appendix B. Solutions to problems.

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Book SynopsisFinancial Econometrics Using Stata is an essential reference for graduate students, researchers, and practitioners who use Stata to perform intermediate or advanced methods. After discussing the characteristics of financial time series, the authors provide introductions to ARMA models, univariate GARCH models, multivariate GARCH models, and applications of these models to financial time series. The last two chapters cover risk management and contagion measures. After a rigorous but intuitive overview, the authors illustrate each method by interpreting easily replicable Stata examples.Table of ContentsIntroduction to financial time series. ARMA models. Modeling volatilities, ARCH models, and GARCH models. Multivariate GARCH models. Risk management. Contagion analysis.

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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 SynopsisConrad Carlberg is a nationally recognized expert on quantitative analysis, data analysis, and management applications such as Microsoft Excel, SAS, and Oracle. He holds a Ph.D. in statistics from the University of Colorado and is a many-time recipient of Microsoft's Excel MVP designation. He is the author of many books, including Business Analysis with Microsoft Excel, Fifth Edition, Statistical Analysis: Microsoft Excel 2016, Regression Analysis Microsoft Excel, and R for Microsoft Excel Users. Carlberg is a Southern California native. After college he moved to Colorado, where he worked for a succession of startups and attended graduate school. He spent two years in the Middle East, teaching computer science and dodging surly camels. After finishing graduate school, Carlberg worked at US West (a Baby Bell) in product management and at Motorola. In 1995 he started a small consulting business (www.conradcarlberg.com)Table of ContentsPrefaceChapter 1 Bayesian Analysis and R: An Overview Bayes Comes Back About Structuring Priors Watching the Jargon Priors, Likelihoods, and Posteriors The Prior The Likelihood Contrasting a Frequentist Analysis with a Bayesian The Frequentist Approach The Bayesian Approach SummaryChapter 2 Generating Posterior Distributions with the Binomial Distribution Understanding the Binomial Distribution Understanding Some Related Functions Working with R's Binomial Functions Using R's dbinom Function Using R's pbinom Function Using R's qbinom Function Using R's rbinom Function Grappling with the Math SummaryChapter 3 Understanding the Beta Distribution Establishing the Beta Distribution in Excel Comparing the Beta Distribution with the Binomial Distribution Decoding Excel's Help Documentation for BETA.DIST Replicating the Analysis in R Understanding dbeta Understanding pbeta Understanding qbeta About Confidence Intervals Applying qbeta to Confidence Intervals Applying BETA.INV to Confidence Intervals SummaryChapter 4 Grid Approximation and the Beta Distribution More on Grid Approximation Setting the Prior Using the Results of the Beta Function Tracking the Shape and Location of the Distribution Inventorying the Necessary Functions Looking Behind the Curtains Moving from the Underlying Formulas to the Functions Comparing Built-in Functions with Underlying Formulas Understanding Conjugate Priors SummaryChapter 5 Grid Approximation with Multiple Parameters Setting the Stage Global Options Local Variables Specifying the Order of Execution Normal Curves, Mu and Sigma Visualizing the Arrays Combining Mu and Sigma Putting the Data Together Calculating the Probabilities Folding in the Prior Inventorying the Results Viewing the Results from Different Perspectives SummaryChapter 6 Regression Using Bayesian Methods Regression a la Bayes Sample Regression Analysis Matrix Algebra Methods Understanding quap Continuing the Code A Full Example Designing the Multiple Regression Arranging a Bayesian Multiple Regression SummaryChapter 7 Handling Nominal Variables Using Dummy Coding Supplying Text Labels in Place of Codes Comparing Group Means SummaryChapter 8 MCMC Sampling Methods Quick Review of Bayesian Sampling Grid Approximation Quadratic Approximation MCMC Gets Up To Speed A Sample MCMC Analysis ulam's Output Validating the Results Getting Trace Plot Charts Summary and Concluding ThoughtsAppendix Installation Instructions for RStan and the rethinking Package on the Windows PlatformGlossary Downloadable Bonus Content Excel Worksheets Book: Statistical Analysis: Microsoft Excel 2016 (PDF) 9780137580989 TOC 10/24/2022

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Book SynopsisAn authoritative go-to resource for students and clinical nutrition practitioners that covers over 270 conditions. Each section provides an overview followed by essential evidence-based information on nutrition management of each condition. Nutrition therapy and practical guidance is presented in a style that is easy-to-navigate and implement.

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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 SynopsisPublisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Need to learn Minitab? Problem Solved!Get started using Minitab right way with help from this hands-on guide. Minitab Demystified walks you through essential Minitab features and shows you how to apply them to solve statistical analysis problems.Featuring coverage of Minitab 16, this practical guide explores the Minitab interface and the full range of Minitab graphics, Distribution models, statistical intervals, hypothesis testing, and sample size calculations are clearly explained. The book covers modeling tools of regression and the design of experiments (DOE) as well as the industrial quality tools of measurement systems analysis, control charts, capability analysis, acceptance sampling, and reliability analysis. Detailed exampTable of ContentsChapter 1. Getting Started with Minitab Statistical SoftwareChapter 2. Analyzing and Comparing Variables with GraphsChapter 3. Exploring the Minitab EnvironmentChapter 4. Selecting and Using Distribution Models in MinitabChapter 5. Making Decisions with IntervalsChapter 6. Testing HypothesesChapter 7. Calculating Sample SizeChapter 8. Fitting Regression ModelsChapter 9. Designing and Analyzing ExperimentsChapter 10. Assessing Measurement SystemsChapter 11. Control ChartingChapter 12. Measuring Process CapabilityChapter 13. Acceptance SamplingChapter 14. Reliability AnalysisFinal ExamAnswers to Quizzes and Final ExamFurther ReadingIndex

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Book Synopsis

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Book Synopsis

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Book Synopsis

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Book SynopsisCovering a range of fields in economics: econometrics, actuarial science, experimental economics, index theory, national accounts, and economic forecasting, this book takes measurement in economics as its central focus. It shows how different and sometimes distinct fields share the same kind of measurement problems.

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