Econometrics and economic statistics Books

Book SynopsisStatistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation. Provides a detailed description of computer algorithms. Provides recently developed computational tools useful for data mining Highlights recent advances in statistical theory and methods that benefit econometric practice. Features examples with real life data. Accompanying software featuring DASC (Data Analysis and Statistical Computing). Essential reading for practitioners in any area of econometrics; business analysts involved in economics and management; and Graduate Table of ContentsForeword xi Preface xiii Acknowledgements xvii 1 Introduction 1 1.1 Nature and Scope of Econometrics 2 1.1.1 What is Econometrics and Why Study Econometrics? 2 1.1.2 Econometrics and Scientific Credibility of Business and Economic Decisions 4 1.2 Types of Economic Problems, Types of Data, and Types of Models 5 1.2.1 Experimental Data from a Marketing Experiment 5 1.2.2 Cross-Section Data: National Sample Survey Data on Consumer Expenditure 6 1.2.3 Non-Experimental Data Taken from Secondary Sources: The Case of Pharmaceutical Industry in India 8 1.2.4 Loan Default Risk of a Customer and the Problem Facing Decision on a Loan Application 9 1.2.5 Panel Data: Performance of Banks in India by the Type of Ownership after Economic Reforms 10 1.2.6 Single Time Series Data: The Bombay Stock Exchange (BSE) Index 12 1.2.7 Multiple Time Series Data: Stock Prices in BRIC Countries 12 1.3 Pattern Recognition and Exploratory Data Analysis 14 1.3.1 Some Basic Issues in Econometric Modeling 14 1.3.2 Exploratory Data Analysis Using Correlations and Scatter Diagrams: The Relative Importance of Managerial Function and Labor 16 1.3.3 Cleaning and Reprocessing Data to Discover Patterns: BSE Index Data 22 1.4 Econometric Modeling: The Roadmap of This Book 24 1.4.1 The Econometric Modeling Strategy 24 1.4.2 Plan of the Book 25 Electronic References for Chapter 1 27 References 27 2 Independent Variables in Linear Regression Models 29 2.1 Brief Review of Linear Regression 29 2.1.1 Brief Review of Univariate Linear Regression 29 2.1.2 Brief Review of Multivariate Linear Regression 38 2.2 Selection of Independent Variable and Stepwise Regression 49 2.2.1 Principles of Selection of Independent Variables 49 2.2.2 Stepwise Regression 52 2.3 Multivariate Data Transformation and Polynomial Regression 57 2.3.1 Linear Regression after Multivariate Data Transformation 57 2.3.2 Polynomial Regression on an Independent Variable 61 2.3.3 Multivariable Polynomial Regression 62 2.4 Column Multicollinearity in Design Matrix and Ridge Regression 65 2.4.1 Effect of Column Multicollinearity of Design Matrix 65 2.4.2 Ridge Regression 68 2.4.3 Ridge Trace Analysis and Ridge Parameter Selection 70 2.4.4 Generalized Ridge Regression 71 2.5 Recombination of Independent Variable and Principal Components Regression 72 2.5.1 Concept of Principal Components Regression 72 2.5.2 Determination of Principal Component 74 Electronic References for Chapter 2 79 References 80 3 Alternative Structures of Residual Error in Linear Regression Models 83 3.1 Heteroscedasticity: Consequences and Tests for Its Existence 85 3.1.1 Consequences of Heteroscedasticity 85 3.1.2 Tests for Heteroscedasticity 87 3.2 Generalized Linear Model with Covariance Being a Diagonal Matrix 90 3.2.1 Diagonal Covariance Matrix and Weighted Least Squares 90 3.2.2 Model with Two Unknown Variances 91 3.2.3 Multiplicative Heteroscedastic Model 92 3.3 Autocorrelation in a Linear Model 95 3.3.1 Linear Model with First-Order Residual Autoregression 96 3.3.2 Autoregressive Conditional Heteroscedasticity (ARCH) Model 101 3.4 Generalized Linear Model with Positive Definite Covariance Matrix 106 3.4.1 Model Definition, Parameter Estimation and Hypothesis Tests 106 3.4.2 Some Equivalent Conditions 108 3.5 Random Effects and Variance Component Model 109 3.5.1 Random Effect Regression Model 109 3.5.2 The Variance Component Model 112 3.5.3 Analysis of Variance Method to Solve Variance Component Model 113 3.5.4 Minimum Norm Quadratic Unbiased Estimation (MINQUE) to Solve Variance Component 121 3.5.5 Maximum Likelihood Method to Solve Variance Component Model 124 Electronic References for Chapter 3 125 References 125 4 Discrete Variables and Nonlinear Regression Model 129 4.1 Regression Model When Independent Variables are Categorical 130 4.1.1 Problem About Wage and Gender Differences 131 4.1.2 Structural Changes in the Savings Function (Use of Categorical Variables in Combination with Continuous Variables) 133 4.1.3 Cross Section Analysis 138 4.1.4 Seasonal Analysis Model 141 4.2 Models with Categorical or Discrete Dependent Variables 144 4.2.1 Linear Model with Binary Dependent Variable 144 4.2.2 Logit Regression Model 148 4.2.3 Probit Regression Model 153 4.2.4 Tobit Regression Model 154 4.3 Nonlinear Regression Model and Its Algorithm 160 4.3.1 The Least Squares Estimate for Nonlinear Regression Model 162 4.3.2 Maximum Likelihood Estimation of Nonlinear Regression Model 164 4.3.3 Equivalence of Maximum Likelihood Estimation and Least Squares Estimation 166 4.4 Nonlinear Regression Models in Practice 169 4.4.1 Growth Curve Models 169 4.4.2 Box–Cox Transformation Model 176 4.4.3 Survival Data and Failure Rate Model 177 4.4.4 Total Factor Productivity (TFP) 181 Electronic References for Chapter 4 188 References 188 5 Nonparametric and Semiparametric Regression Models 193 5.1 Nonparametric Regression and Weight Function Method 194 5.1.1 The Concept of Nonparametric Regression 194 5.1.2 Weight Function Method 196 5.2 Semiparametric Regression Model 199 5.2.1 Linear Semiparametric Regression Model 202 5.2.2 Single-Index Semiparametric Regression Model 205 5.3 Stochastic Frontier Regression Model 208 5.3.1 Stochastic Frontier Linear Regression Model and Asymptotically Efficient Estimator of Its Parameters 208 5.3.2 Semiparametric Stochastic Frontier Model 210 Electronic References for Chapter 5 212 References 213 6 Simultaneous Equations Models and Distributed Lag Models 215 6.1 Simultaneous Equations Models and Inconsistency of OLS Estimators 216 6.1.1 Demand-and-Supply Model, Keynesian Model and Wage-Price Model (Phillips Curve) 218 6.1.2 Macroeconomic IS Model, LM Model and Klein’s Econometric Model 220 6.1.3 Inconsistency of OLS Estimation 222 6.2 Statistical Inference for Simultaneous Equations Models 223 6.2.1 Indirect Least Squares and Generalized Least Squares 224 6.2.2 Two Stage Least Squares 229 6.3 The Concepts of Lag Regression Models 235 6.3.1 Consumption Lag 236 6.3.2 Inflation Lag 237 6.3.3 Deposit Re-Creation 238 6.4 Finite Distributed Lag Models 239 6.4.1 Estimation of Distributed Lag Models When the Lag Length is Known and Finite 239 6.4.2 The Determination of Distributed Lag Length 239 6.5 Infinite Distributed Lag Models 242 6.5.1 Adaptive Expectations Model and Partial Adjustment Model 243 6.5.2 Koyck Transformation and Estimation of Geometric Lag Models 245 Electronic References for Chapter 6 249 References 250 7 Stationary Time Series Models 253 7.1 Auto-Regression Model AR( p) 255 7.1.1 AR( p) Model and Stationarity 255 7.1.2 Auto-Covariance Function and Autocorrelation Function of AR( p) Model 258 7.1.3 Spectral Density of AR( p) Model and Partial Correlation Coefficient 263 7.1.4 Estimation of Parameters for AR( p) Model with Known Order p 267 7.1.5 Order Identification for AR( p) Process 274 7.2 Moving Average Model MA(q) 276 7.2.1 MA(q) Model and Its Properties 276 7.2.2 Parameter Estimation of MA(q) Model When the Order q is Known 278 7.2.3 Spectral Density Estimation for MA(q) Process 282 7.2.4 Order Identification for MA(q) Process 284 7.3 Auto-Regressive Moving-Average Process ARMA( p, q) 285 7.3.1 ARMA(p, q) Model and Its Properties 285 7.3.2 Parameter Estimations for ARMA(p, q) Model 288 7.3.3 Test for ARMA( p, q) Model 291 7.3.4 Order Identification for ARMA( p, q) Model 291 7.3.5 Univariate Time Series Modeling: The Basic Issues and Approaches 292 Electronic References for Chapter 7 293 References 293 8 Multivariate and Nonstationary Time Series Models 297 8.1 Multivariate Stationary Time Series Model 299 8.1.1 General Description of Multivariable Stationary Time Series Model 299 8.1.2 Estimation of Mean and Autocovariance Function of Multivariate Stationary Time Series 300 8.1.3 Vector Autoregression Model of Order p: VAR( p) 301 8.1.4 Wold Decomposition and Impulse-Response 301 8.1.5 Variance Decomposition with VAR( p) 306 8.1.6 Granger Causality with VAR(p) Specification 309 8.2 Nonstationary Time Series 311 8.2.1 Stochastic Trends and Unit Root Processes 311 8.2.2 Test for Unit Root Hypothesis 314 8.3 Cointegration and Error Correction 321 8.3.1 The Concept and Representation of Cointegration 322 8.3.2 Simultaneous (Structural) Equation System (SES) and Vector Auto Regression (VAR) 324 8.3.3 Cointegration and Error Correction Representation 325 8.3.4 Estimation of Parameters of Cointegration Process 329 8.3.5 Test of Hypotheses on the Number of Cointegrating Equations 330 8.4 Autoregression Conditional Heteroscedasticity in Time Series 333 8.4.1 ARCH Model 334 8.4.2 Generalized ARCH Model—GARCH Model 338 8.4.3 Other Generalized Forms of ARCH Model 342 8.5 Mixed Models of Multivariate Regression with Time Series 346 8.5.1 Mixed Model of Multivariate Regression with Time Series 346 8.5.2 Mixed Model of Multivariate Regression and Cointegration with Time Series 349 Electronic References for Chapter 8 353 References 353 9 Multivariate Statistical Analysis and Data Analysis 357 9.1 Model of Analysis of Variance 358 9.1.1 Single Factor Analysis of Variance Model 358 9.1.2 Two Factor Analysis of Variance with Non-Repeated Experiment 361 9.1.3 Two Factor Analysis of Variance with Repeated Experiment 364 9.2 Other Multivariate Statistical Analysis Models 370 9.2.1 Discriminate Analysis Model 370 9.2.2 Factor Analysis Model 376 9.2.3 Principal Component Analysis and Multidimensional Scaling Method 380 9.2.4 Canonical Correlation Analysis 384 9.3 Customer Satisfaction Model and Path Analysis 387 9.3.1 Customer Satisfaction Model and Structural Equations Model 387 9.3.2 Partial Least Square and the Best Iterative Initial Value 391 9.3.3 Definite Linear Algorithm for SEM 399 9.3.4 Multi-Layers Path Analysis Model 402 9.4 Data Analysis and Process 404 9.4.1 Panel Data Analysis 404 9.4.2 Truncated Data Analysis 405 9.4.3 Censored Data Analysis 406 9.4.4 Duration Data Analysis 407 9.4.5 High Dimensional Data Visualization 409 Electronic References for Chapter 9 412 References 413 10 Summary and Further Discussion 415 10.1 About Probability Distributions: Parametric and Non-Parametric 416 10.1.1 Distributions of Functions of Random Variables 416 10.1.2 Parametric, Non-Parametric, and Semi-Parametric Specification of Distributions 417 10.1.3 Non-Parametric Specification of Density Functions 418 10.2 Regression 421 10.2.1 Regression as Conditional Mean of the Dependent Variable 421 10.2.2 Regressions with Homoscedastic and Heteroscedastic Variance 421 10.2.3 General Regression Functions: Quantiles and Quantile Regression 423 10.2.4 Design of Experiments, Regression, and Analysis of Variance 424 10.3 Model Specification and Prior Information 425 10.3.1 Data Generation Process (DGP) and Economic Structure 426 10.3.2 Deterministic but Unknown Parameters and Model Specification as a Maintained Hypothesis 428 10.3.3 Stochastic Prior Information on Unknown Parameters 429 10.4 Classical Theory of Statistical Inference 430 10.4.1 The Likelihood Function, Sufficient Statistics, Complete Statistics, and Ancillary Statistics 430 10.4.2 Different Methods of Estimation of Unknown Parameters 434 10.4.3 Biased and Unbiased Estimators, Consistency of Estimators 437 10.4.4 Information Limit to Variance of an Estimator, Cramer-Rao Bound, and Rao-Blackwell Theorem 438 10.4.5 Approximate Sufficiency and Robust Estimation 440 10.5 Computation of Maximum Likelihood Estimates 441 10.5.1 Newton-Raphson Method and Rao’s Method of Scoring 442 10.5.2 Davidon-Fletcher-Powell-Reeves Conjugate Gradient Procedure 443 10.5.3 Estimates of the Variance Covariance Matrix of Maximum Likelihood Estimators 444 10.6 Specification Searches 445 10.6.1 Choice Between Alternate Specifications: Akaike and Schwarz Information Criteria 445 10.6.2 Generalized Information and Complexity-Based Model Choice Criterion 447 10.6.3 An Illustration of Model Choice: Engel Curve for Food Consumption in India 448 10.7 Resampling and Sampling Distributions – The Bootstraps Method 450 10.7.1 The Concept of Resampling and the Bootstraps Method 450 10.7.2 Bootstraps in Regression Models 452 10.8 Bayesian Inference 454 10.8.1 The Bayes Rule 454 10.8.2 Choice of Prior Probability Distribution for the Parameter 455 10.8.3 Bayesian Concepts for Statistical Inference 456 Electronic References for Chapter 10 457 References 458 Index 461

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Book SynopsisA guide to the growing importance of extreme value risk theory, methods, and applications in the financial sector Presenting a uniquely accessible guide, Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications features a combination of the theory, methods, and applications of extreme value theory (EVT) in finance and a practical understanding of market behavior including both ordinary and extraordinary conditions. Beginning with a fascinating history of EVTs and financial modeling, the handbook introduces the historical implications that resulted in the applications and then clearly examines the fundamental results of EVT in finance. After dealing with these theoretical results, the handbook focuses on the EVT methods critical for data analysis. Finally, the handbook features the practical applications and techniques and how these can be implemented in financial markets. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications includes: Over 40 contributions from international experts in the areas of finance, statistics, economics, business, insurance, and risk managementTopical discussions on univariate and multivariate case extremes as well as regulation in financial marketsExtensive references in order to provide readers with resources for further studyDiscussions on using R packages to compute the value of risk and related quantities The book is a valuable reference for practitioners in financial markets such as financial institutions, investment funds, and corporate treasuries, financial engineers, quantitative analysts, regulators, risk managers, large-scale consultancy groups, and insurers. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications is also a useful textbook for postgraduate courses on the methodology of EVTs in finance.Table of ContentsAbout the Editor xiii About the Contributors xv 1 Introduction 1François Longin 1.1 Extremes 1 1.2 History 2 1.3 Extreme value theory 2 1.4 Statistical estimation of extremes 2 1.5 Applications in finance 4 1.6 Practitioners’ points of view 6 1.7 A broader view on modeling extremes 6 1.8 Final words 7 1.9 Thank you note 7 References 8 2 Extremes Under Dependence—Historical Development and Parallels with Central Limit Theory 11M.R. Leadbetter 2.1 Introduction 11 2.2 Classical (I.I.D.) central limit and extreme value theories 12 2.3 Exceedances of levels, kth largest values 14 2.4 CLT and EVT for stationary sequences, bernstein’s blocks, and strong mixing 15 2.5 Weak distributional mixing for EVT, D(un), extremal index 18 2.6 Point process of level exceedances 19 2.7 Continuous parameter extremes 20 References 22 3 The Extreme Value Problem in Finance: Comparing the Pragmatic Program with the Mandelbrot Program 25Christian Walter 3.1 The extreme value puzzle in financial modeling 25 3.2 The sato classification and the two programs 28 3.3 Mandelbrot’s program: A fractal approach 34 3.4 The Pragmatic Program: A data-driven approach 39 3.5 Conclusion 47 Acknowledgments 48 References 48 4 Extreme Value Theory: An Introductory Overview 53Isabel Fraga Alves and Cláudia Neves 4.1 Introduction 53 4.2 Univariate case 56 4.3 Multivariate case: Some highlights 84 Further reading 90 Acknowledgments 90 References 90 5 Estimation of the Extreme Value Index 97Beirlant J., Herrmann K., and Teugels J.L. 5.1 Introduction 97 5.2 The main limit theorem behind extreme value theory 98 5.3 Characterizations of the max-domains of attraction and extreme value index estimators 99 5.4 Consistency and asymptotic normality of the estimators 103 5.5 Second-order reduced-bias estimation 104 5.6 Case study 106 5.7 Other topics and comments 108 References 111 6 Bootstrap Methods in Statistics of Extremes 117M. Ivette Gomes, Frederico Caeiro, Lígia Henriques-Rodrigues, and B.G. Manjunath 6.1 Introduction 117 6.2 A few details on EVT 119 6.3 The bootstrap methodology in statistics of univariate extremes 127 6.4 Applications to simulated data 133 6.5 Concluding remarks 133 Acknowledgments 135 References 135 7 Extreme Values Statistics for Markov Chains with Applications to Finance and Insurance 139Patrice Bertail, Stéphan Clémençon, and Charles Tillier 7.1 Introduction 139 7.2 On the (pseudo) regenerative approach for markovian data 141 7.3 Preliminary results 151 7.4 Regeneration-based statistical methods for extremal events 154 7.5 The extremal index 156 7.6 The regeneration-based hill estimator 159 7.7 Applications to ruin theory and financial time series 161 7.8 An application to the CAC40 165 7.9 Conclusion 167 References 167 8 Lévy Processes and Extreme Value Theory 171Olivier Le Courtois and Christian Walter 8.1 Introduction 171 8.2 Extreme value theory 173 8.3 Infinite divisibility and Lévy processes 178 8.4 Heavy-tailed Lévy processes 182 8.5 Semi-heavy-tailed Lévy processes 184 8.6 Lévy processes and extreme values 187 8.7 Conclusion 192 References 192 9 Statistics of Extremes: Challenges and Opportunities 195M. de Carvalho 9.1 Introduction 195 9.2 Statistics of bivariate extremes 196 9.3 Models based on families of tilted measures 204 9.4 Miscellanea 209 References 211 10 Measures of Financial Risk 215S.Y. Novak 10.1 Introduction 215 10.2 Traditional measures of risk 215 10.3 Risk estimation 218 10.4 “Technical analysis” of financial data 222 10.5 Dynamic risk measurement 226 10.6 Open problems and further research 234 10.7 Conclusion 235 Acknowledgment 235 References 235 11 On the Estimation of the Distribution of Aggregated Heavy-Tailed Risks: Application to Risk Measures 239Marie Kratz 11.1 Introduction 239 11.2 A brief review of existing methods 245 11.3 New approaches: Mixed limit theorems 247 11.4 Application to risk measures and comparison 269 11.5 Conclusion 277 References 279 12 Estimation Methods for Value at Risk 283Saralees Nadarajah and Stephen Chan 12.1 Introduction 283 12.2 General properties 289 12.3 Parametric methods 300 12.4 Nonparametric methods 326 12.5 Semiparametric methods 332 12.6 Computer software 344 12.7 Conclusions 347 Acknowledgment 347 References 347 13 Comparing Tail Risk and Systemic Risk Profiles for Different Types of U.S. Financial Institutions 357Stefan Straetmans and Thanh Thi Huyen Dinh 13.1 Introduction 357 13.2 Tail risk and systemic risk indicators 361 13.3 Tail risk and systemic risk estimation 364 13.4 Empirical results 368 13.5 Conclusions 381 References 382 14 Extreme Value Theory and Credit Spreads 391Wesley Phoa 14.1 Preliminaries 391 14.2 Tail behavior of credit markets 394 14.3 Some multivariate analysis 398 14.4 Approximating value at risk for credit portfolios 401 14.5 Other directions 403 References 404 15 Extreme Value Theory and Risk Management in Electricity Markets 405Kam Fong Chan and Philip Gray 15.1 Introduction 405 15.2 Prior literature 407 15.3 Specification of VaR estimation approaches 409 15.4 Empirical analysis 413 15.5 Conclusion 422 Acknowledgment 423 References 423 16 Margin Setting and Extreme Value Theory 427John Cotter and Kevin Dowd 16.1 Introduction 427 16.2 Margin setting 428 16.3 Theory and methods 430 16.4 Empirical results 434 16.5 Conclusions 439 Acknowledgment 440 References 440 17 The Sortino Ratio and Extreme Value Theory: An Application to Asset Allocation 443G. Geoffrey Booth and John Paul Broussard 17.1 Introduction 443 17.2 Data definitions and description 446 17.3 Performance ratios and their estimations 451 17.4 Performance measurement results and implications 456 17.5 Concluding remarks 460 Acknowledgments 461 References 461 18 Portfolio Insurance: The Extreme Value Approach Applied to the CPPI Method 465Philippe Bertrand and Jean-Luc Prigent 18.1 Introduction 465 18.2 The CPPI method 467 18.3 CPPI and quantile hedging 472 18.4 Conclusion 481 References 481 19 The Choice of the Distribution of Asset Returns: How Extreme Value Can Help? 483François Longin 19.1 Introduction 483 19.2 Extreme value theory 485 19.3 Estimation of the tail index 488 19.4 Application of extreme value theory to discriminate among distributions of returns 490 19.5 Empirical results 493 19.6 Conclusion 501 References 501 20 Protecting Assets Under Non-Parametric Market Conditions 507Jean-Marie Choffray and Charles Pahud de Mortanges 20.1 Investors’ “known knowns” 509 20.2 Investors’ “known unknowns” 512 20.3 Investors’ “unknown knowns” 515 20.4 Investors’ “unknown unknowns” 518 20.5 Synthesis 522 References 523 21 EVT Seen by a Vet: A Practitioner’s Experience on Extreme Value Theory 525Jean-François Boulier 21.1 What has the vet done? 525 21.2 Why use EVT? 526 21.3 What EVT could additionally bring to the party? 528 21.4 A final thought 528 References 528 22 The Robotization of Financial Activities: A Cybernetic Perspective 529Hubert Rodarie 22.1 An increasingly complex system 530 22.2 Human error 532 22.3 Concretely, what do we need to do to transform a company into a machine? 534 References 543 23 Two Tales of Liquidity Stress 545Jacques Ninet 23.1 The french money market fund industry. How history has shaped a potentially vulnerable framework 546 23.2 The 1992–1995 forex crisis 547 23.3 Four mutations paving the way for another meltdown 549 23.4 The subprime crisis spillover. How some MMFs were forced to lock and some others not 551 23.5 Conclusion. What lessons can be drawn from these two tales? 552 Further Readings 553 24 Managing Operational Risk in the Banking Business – An Internal Auditor Point of View 555Maxime Laot Further Reading 559 References 560 Annexes 560 25 Credo Ut Intelligam 563Henri Bourguinat and Eric Briys 25.1 Introduction 563 25.2 “Anselmist” finance 563 25.3 Casino or dance hall? 565 25.4 Simple-minded diversification 566 25.5 Homo sapiens versus homo economicus 568 Acknowledgement 569 References 569 26 Bounded Rationalities, Routines, and Practical as well as Theoretical Blindness: On the Discrepancy Between Markets and Corporations 571Laurent Bibard 26.1 Introduction: Expecting the unexpected 571 26.2 Markets and corporations: A structural and self-disruptive divergence of interests 572 26.3 Making a step back from a dream: On people expectations 574 26.4 How to disentangle people from a unilateral short-term orientation? 578 References 580 Name Index 583 Subject Index 593

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Book SynopsisProviding a solid managerial perspective, PURCHASING AND SUPPLY CHAIN MANAGEMENT, 6e draws from the authors' firsthand experiences and relationships with executives and practitioners worldwide to present the most current and complete coverage of today's supply management process. The text includes critical developments from the field, such as cases from emerging healthcare and service industries, procure-to-pay redesign, supply risk, innovation, sustainability, collaboration, and much more. It also examines key changes in supply management and the impact of the global economy and ongoing business uncertainty on continuous cost and value management across the supply chain. Numerous real-world cases and captivating examples give you contextual insights and knowledge into the strategies, processes, and practices of supply management. PURCHASING AND SUPPLY CHAIN MANAGEMENT, 6E equips future managers with a thorough understanding of the impact that purchasing and supply chain management havTable of ContentsPart 1: INTRODUCTION. 1. Introduction to Purchasing and Supply Chain Management. Part 2: PURCHASING OPERATIONS AND STRUCTURE. 2. The Purchasing Process. 3. Purchasing Policies and Procedures. 4. Supply Management Integration for Competitive Advantage. 5. Purchasing and Supply Management Organization. Part 3: STRATEGIC SOURCING. 6. Supply Management and Commodity Strategy Development. 7. Supplier Evaluation and Selection. 8. Supplier Quality Management. 9. Supplier Management and Development: Creating a World-Class Supply Base. 10. Worldwide Sourcing. Part 4: STRATEGIC SOURCING PROCESS. 11. Strategic Cost Management. 12. Purchasing and Supply Chain Analysis: Tools and Techniques. 13. Negotiation and Conflict Management. 14. Contract Management. 15. Purchasing Law and Ethics. Part 5: CRITICAL SUPPLY CHAIN ELEMENTS. 16. Lean Supply Chain Management. 17. Purchasing Services. 18. Supply Chain Information Systems and Electronic Sourcing. 19. Performance Measurement and Evaluation. Part 6: FUTURE DIRECTIONS. 20. Purchasing and Supply Strategy Trends.

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Book SynopsisHelps you put statistical theories into practice. This calculus-based book offers a comprehensive introduction to probability and statistics while demonstrating how to apply concepts, models, and methodologies in today's engineering and scientific workplaces.Table of Contents1. OVERVIEW AND DESCRIPTIVE STATISTICS. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. 2. PROBABILITY. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. 3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Random Variables. Probability Distributions for Discrete Random Variables. Expected Values. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution. 4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Exponential and Gamma Distributions. Other Continuous Distributions. Probability Plots. 5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES. Jointly Distributed Random Variables. Expected Values, Covariance, and Correlation. Statistics and Their Distributions. The Distribution of the Sample Mean. The Distribution of a Linear Combination. 6. POINT ESTIMATION. Some General Concepts of Point Estimation. Methods of Point Estimation. 7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. Basic Properties of Confidence Intervals. Large-Sample Confidence Intervals for a Population Mean and Proportion. Intervals Based on a Normal Population Distribution. Confidence Intervals for the Variance and Standard Deviation of a Normal Population. 8. TESTS OF HYPOTHESIS BASED ON A SINGLE SAMPLE. Hypotheses and Test Procedures. z Tests for Hypotheses About a Population Mean. The One-Sample t Test. Tests Concerning a Population Proportion. Further Aspects of Hypothesis Testing. 9. INFERENCES BASED ON TWO SAMPLES. z Tests and Confidence Intervals for a Difference between Two Population Means. The Two-Sample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference between Population Proportions. Inferences Concerning Two Population Variances. 10. THE ANALYSIS OF VARIANCE. Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA. 11. MULTIFACTOR ANALYSIS OF VARIANCE. Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij > 1. Three-Factor ANOVA 11. 4 2p Factorial Experiments. 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter ss1. Inferences Concerning Y*x* and the Prediction of Future Y Values. Correlation. 13. NONLINEAR AND MULTIPLE REGRESSION. Assessing Model Adequacy. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression. 14. GOODNESS-OF-FIT TESTS AND CATEGORICAL DATA ANALYSIS. Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables 15. DISTRIBUTION-FREE PROCEDURES. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. 16. QUALITY CONTROL METHODS. General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures. Acceptance Sampling.

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Book SynopsisCounting Bounty highlights a widespread blindspot: most of us overlook land and its power to twist an economy. Householders typically spend most of their budget on land without awareness. The story begins with the official and academic efforts to minimize the total worth of Earth in America. A perusal of the historical relationship between the elite and the intellectual shows that "paying the piper" is the norm, even up to the present. Using a slew of statistics and others’ research findings, this book tracks rent to its recipients, the rentiers who own much and wield power. Aware reformers can address pressing problems by tapping land value. Watching rent flow sheds light on how economies operate, why they sometimes fail, and what a society can do about it.Trade Review"The vast number of references and the apt details reflects the enormous amount of expertise and time which has been invested in it." -- Team PlanningTank"Land and money are the two main elements in political economy. Jeff Smith has been digging into "the land problem" for a long time and his expertise in that subject is without question. His findings deserve a wide audience as we struggle to bring into being a more just, equitable, and sustainable world order. In this book, Smith reveals many little-known facts about things that affect our lives, particularly land ownership, the process of rent-seeking, the concentration of wealth, and the corruption of politics, education, and other aspects of society by which the one percent continue to control the general framework of public thought." -- Thomas H Greco, author of The End of Money and the Future of Civilization"All property is made partly out of natural resources that aren't 'naturally' anybody's property. The government makes them into property. It gives them to private interests for free, and they sell it back to us for money. That might be an opportunity for corruption. This book explains problems caused by the way the world's governments dole out resources to the privileged and the potential of a better resource policy." -- Karl Widerquist, an American political philosopher and economist at Georgetown University-Qatar, is co-founder if the US Basic Income Guarantee (USBIG) Network, has been co-chair of the Basic Income Earth Network (BIEN) since 2008, and co-founded Basic Income News in 2011

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Book SynopsisLa econometría aplicada es la ciencia de los datos en su estado original, y engloba los métodos estadísticos que se usan en economía para desentrañar causas y efectos de las actividades humanas. Con un lenguaje accesible y algunas dosis de humor con sabor a kung-fu, esta obra expone las herramientas esenciales del análisis econométrico y desvela por qué la econometría es una disciplina tan apasionante y útil.¿Mejoran la salud los seguros médicos? ¿Son mejores las universidades de élite que otro tipo de centros académicos? Cuando la banca privada se tambalea, y los inversores toman el dinero y huyen, ¿deben acudir en su ayuda los bancos centrales? Angrist y Pischke nos muestran que, con los métodos adecuados, la econometría es capaz de ofrecernos respuestas a preguntas tan dispares como estas.

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