International Edition Econometric Analysis
InternationalEditionInternational EditionThis is a special edition of an established titlewidely used by colleges and universitiesthroughout the world. Pearson published thisexclusive edition for the benefit of studentsoutside the United States and Canada. If youpurchased this book within the United Statesor Canada you should be aware that it hasbeen imported without the approval of thePublisher or the Author.GreeneEconometric AnalysisThe editorial team at Pearson has worked closely witheducators around the globe to inform students of theever-changing world in a broad variety of disciplines.Pearson Education offers this product to theinternational market, which may or may not includealterations from the United States version.Seventh EditionPearson International EditionInternational EditionEconometricAnalysisSeventh EditionWilliam H. Greene
Greene-2140242A01 GREE3568 07 GE FMJanuary 19, 201120:15SEVENTH EDITIONECONOMETRIC ANALYSISINTERNATIONAL EDITIONQWilliam H. GreeneNew York University
Greene-2140242A01 GREE3568 07 GE FMFebruary 16, 201115:22For Margaret and Richard GreeneEditorial Director: Sally YaganEditor in Chief: Donna BattistaAcquisitions Editor: Adrienne D’AmbrosioSenior International Acquisitions Editor:Laura DentEditorial Project Manager: Jill KolongowskiDirector of Marketing: Patrice JonesSenior Marketing Manager: Lori DeShazoInternational Marketing Manager: Dean ErasmusManaging Editor: Nancy FentonProduction Project Manager: Carla ThompsonManufacturing Director: Evelyn BeatonSenior Manufacturing Buyer: Carol MelvilleCreative Director: Christy MahonCover Designer: Jodi NotowitzCover Image: Robert Adrian Hillman/AlamyPermissions Project Supervisor: Michael JoyceMedia Producer: Melissa HonigAssociate Production Project Manager:Alison EusdenFull-Service Project Management:MPS Limited, a Macmillan CompanyCover Printer: Lehigh-PhoenixColor/HagerstownPearson Education LimitedEdinburgh Gate, HarlowEssex CM20 2JE, Englandand Associated Companies throughout the worldVisit us on the World Wide Web at: www.pearsoned.co.uk Pearson Education Limited 2012The right of William H. Greene to be identified as author of this work has been asserted by him inaccordance with the Copyright, Designs and Patents Act 1988.Authorised adaptation from the United States edition, entitled Econometric Analysis,ISBN 978-0-13-139538-1 by William H. Greene published by Pearson Education, publishing asPrentice Hall 2012.All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, ortransmitted in any form or by any means, electronic, mechanical, photocopying, recording orotherwise, without either the prior written permission of the publisher or a licence permittingrestricted copying in the United Kingdom issued by the Copyright Licensing Agency Ltd, SaffronHouse, 6–10 Kirby Street, London EC1N 8TS.All trademarks used herein are the property of their respective owners. The use of any trademark inthis text does not vest in the author or publisher any trademark ownership rights in such trademarks,nor does the use of such trademarks imply any affiliation with or endorsement of this book by suchowners.Credits and acknowledgements borrowed from other sources and reproduced, with permission, in thistextbook appear on an appropriate page within text.ISBN 13: 978-0-273-75356-8ISBN 10: 0-273-75356-8British Library Cataloguing-in-Publication DataA catalogue record for this book is available from the British Library10 9 8 7 6 5 4 3 2 115 14 13 12 11Typeset in 10/12 Times by MPS Limited, a Macmillan Company.Printed and bound by Courier/Westford in The United States of AmericaThe publisher’s policy is to use paper manufactured from sustainable forests.
Greene-2140242A01 GREE3568 07 GE FMFebruary 8, 201121:6PEARSON SERIES IN e/ParkinFoundations of Economics*Berck/HelfandThe Economics of the EnvironmentBierman/FernandezGame Theory with erber/WinklerThe Economics of Women,Men and WorkBoardman/Greenberg/Vining/WeimerCost-Benefit AnalysisBoyerPrinciples of Transportation EconomicsBransonMacroeconomic Theory and PolicyBrock/AdamsThe Structure of American IndustryBrucePublic Finance and the AmericanEconomyCarlton/PerloffModern Industrial OrganizationCase/Fair/OsterPrinciples of Economics*Caves/Frankel/JonesWorld Trade and Payments:An IntroductionChapmanEnvironmental Economics:Theory, Application, and PolicyCooter/UlenLaw & EconomicsDownsAn Economic Theory of DemocracyEhrenberg/SmithModern Labor Economics for ManagersFolland/Goodman/StanoThe Economics of Health and HealthCareFortSports EconomicsFroyenMacroeconomicsFusfeldThe Age of the EconomistGerberInternational Economics*GordonMacroeconomics*GreeneEconometric AnalysisGregoryEssentials of EconomicsGregory/StuartRussian and Soviet EconomicPerformance and Structure* denotestitlesHartwick/OlewilerThe Economics of NaturalResource UseHeilbroner/MilbergThe Making of the Economic SocietyHeyne/Boettke/PrychitkoThe Economic Way of ThinkingHoffman/AverettWomen and the Economy:Family, Work, and PayHoltMarkets, Games and StrategicBehaviorHubbard/O’BrienEconomics*Money, Banking, and the FinancialSystem*Hughes/CainAmerican Economic HistoryHusted/MelvinInternational EconomicsJehle/RenyAdvanced Microeconomic TheoryJohnson-LansA Health Economics PrimerKeat/YoungManagerial EconomicsKleinMathematical Methods for EconomicsKrugman/Obstfeld/MelitzInternational Economics:Theory & Policy*LaidlerThe Demand for MoneyLeeds/von AllmenThe Economics of SportsLeeds/von ics*LynnEconomic Development: Theory andPractice for a Divided WorldMillerEconomics Today*Understanding ModernEconomicsMiller/BenjaminThe Economics of Macro IssuesMiller/Benjamin/NorthThe Economics of Public IssuesMills/HamiltonUrban EconomicsMishkinThe Economics of Money, Banking, andFinancial Markets*The Economics of Money, Banking, andFinancial Markets, Business SchoolEdition*Macroeconomics: Policy and Practice*MurrayEconometrics: A Modern IntroductionNafzigerThe Economics of omics: Principles, Applications roeconomics: Theory andApplications with Calculus*Perman/Common/McGilvray/MaNatural Resources andEnvironmental EconomicsPhelpsHealth hackelford/Stamos/SchneiderEconomics: A Tool for CriticallyUnderstanding SocietyRitter/Silber/UdellPrinciples of Money, Banking &Financial Markets*RobertsThe Choice: A Fable of Free Trade andProtectionRohlfIntroduction to Economic ReasoningRuffin/GregoryPrinciples of EconomicsSargentRational Expectations and InflationSawyer/SprinkleInternational EconomicsSchererIndustry Structure, Strategy, and PublicPolicySchillerThe Economics of Poverty andDiscriminationShermanMarket RegulationSilberbergPrinciples of MicroeconomicsStock/WatsonIntroduction to EconometricsIntroduction to Econometrics, BriefEditionStudenmundUsing Econometrics: A Practical GuideTietenberg/LewisEnvironmental and Natural ResourceEconomicsEnvironmental Economics and PolicyTodaro/SmithEconomic strial Organization: Theory andPracticeWeilEconomic GrowthWilliamsonMacroeconomicsLog onto www.myeconlab.com to learn more
Greene-2140242A01 GREE3568 07 GE FMJanuary 19, 201120:15BRIEF CONTENTSQExamples and ApplicationsPreface33Part IThe Linear Regression ModelChapter 1Chapter 2Econometrics41The Linear Regression ModelChapter 3Chapter 4Chapter 5Chapter 6Least Squares66The Least Squares Estimator91Hypothesis Tests and Model SelectionFunctional Form and Structural ChangeChapter 7Chapter 8Nonlinear, Semiparametric, and NonparametricRegression Models221Endogeneity and Instrumental Variable EstimationPart IIGeneralized Regression Model and Equation SystemsChapter 9Chapter 10The Generalized Regression Model and HeteroscedasticitySystems of Equations330Chapter 11Models for Panel DataPart IIIEstimation MethodologyChapter 12Estimation Frameworks in EconometricsChapter 13Chapter 16Minimum Distance Estimation and the GeneralizedMethod of Moments495Maximum Likelihood Estimation549Simulation-Based Estimation and Inference and RandomParameter Models643Bayesian Estimation and Inference695Part IVCross Sections, Panel Data, and MicroeconometricsChapter 17Chapter 18Chapter 19Discrete Choice721Discrete Choices and Event Counts800Limited Dependent Variables—Truncation, Censoring,and Sample Selection873Chapter 14Chapter 1542551148189259383472297
Greene-2140242A01 GREE3568 07 GE FMJune 1, 201115:23Brief ContentsPart VTime Series and MacroeconometricsChapter 20Chapter 21Serial CorrelationNonstationary DataPart VIAppendices943982Appendix A Matrix Algebra1013Appendix B Probability and Distribution TheoryAppendix C Estimation and Inference10871055Appendix D Large-Sample Distribution Theory1106Appendix E Computation and Optimization1129Appendix F Data Sets Used in ApplicationsReferences1155Combined Author and Subject Index121111495
Greene-2140242A01 GREE3568 07 GE FMJanuary 19, 201120:15CONTENTSQExamples and ApplicationsPreface2533PART I The Linear Regression ModelCHAPTER 1 Econometrics411.1Introduction411.2The Paradigm of Econometrics1.3The Practice of Econometrics1.4Econometric Modeling441.51.64143Plan of the Book47Preliminaries491.6.11.6.21.6.3Numerical Examples 49Software and Replication 49Notational Conventions 49CHAPTER 2 The Linear Regression Model512.1Introduction512.2The Linear Regression Model522.3Assumptions of the Linear Regression Model2.42.3.1Linearity of the Regression Model 552.3.2Full Rank 592.3.3Regression 602.3.4Spherical Disturbances 612.3.5Data Generating Process for the Regressors2.3.6Normality 632.3.7Independence 64Summary and Conclusions65CHAPTER 3 Least Squares663.1Introduction663.2Least Squares Regression663.2.1The Least Squares Coefficient Vector6556763
Greene-2140242A01 GREE3568 07 GE FMJanuary 19, 201120:15Contents3.33.43.53.63.73.2.2Application: An Investment Equation 683.2.3Algebraic Aspects of the Least Squares Solution 703.2.4Projection 71Partitioned Regression and Partial Regression72Partial Regression and Partial Correlation Coefficients76Goodness of Fit and the Analysis of Variance793.5.1The Adjusted R-Squared and a Measure of Fit 823.5.2R-Squared and the Constant Term in the Model 843.5.3Comparing Models 85Linearly Transformed Regression86Summary and Conclusions87CHAPTER 4 The Least Squares Estimator914.1Introduction914.2Motivating Least Squares924.2.1The Population Orthogonality Conditions 924.2.2Minimum Mean Squared Error Predictor 934.2.3Minimum Variance Linear Unbiased Estimation4.3Finite Sample Properties of Least Squares944.44.5944.3.1Unbiased Estimation 954.3.2Bias Caused by Omission of Relevant Variables 964.3.3Inclusion of Irrelevant Variables 984.3.4The Variance of the Least Squares Estimator 984.3.5The Gauss–Markov Theorem 1004.3.6The Implications of Stochastic Regressors 1004.3.7Estimating the Variance of the Least Squares Estimator 1014.3.8The Normality Assumption 103Large Sample Properties of the Least Squares Estimator1034.4.1Consistency of the Least Squares Estimator of β 1034.4.2Asymptotic Normality of the Least Squares Estimator 1054.4.3Consistency of s2 and the Estimator of Asy. Var[b] 1074.4.4Asymptotic Distribution of a Function of b: The DeltaMethod 1084.4.5Asymptotic Efficiency 1094.4.6Maximum Likelihood Estimation 113Interval Estimation1154.5.14.5.24.5.34.67Forming a Confidence Interval for a Coefficient 116Confidence Intervals Based on Large Samples 118Confidence Interval for a Linear Combination of Coefficients:The Oaxaca Decomposition 119Prediction and Forecasting1204.6.1Prediction Intervals 1214.6.2Predicting y When the Regression Model Describes Log y 121
Greene-2140242A01 GREE3568 07 GE FM8January 19, 201120:15Contents4.6.34.74.8Prediction Interval for y When the Regression Model DescribesLog y 1234.6.4Forecasting 127Data Problems1284.7.1Multicollinearity 1294.7.2Pretest Estimation 1314.7.3Principal Components 1324.7.4Missing Values and Data Imputation 1344.7.5Measurement Error 1374.7.6Outliers and Influential Observations 139Summary and Conclusions142CHAPTER 5 Hypothesis Tests and Model Selection5.1Introduction1485.2Hypothesis Testing 5Restrictions and Hypotheses 149Nested Models 150Testing Procedures—Neyman–Pearson Methodology 151Size, Power, and Consistency of a Test 151A Methodological Dilemma: Bayesian versus Classical Testing152Two Approaches to Testing Hypotheses152Wald Tests Based on the Distance Measure1555.4.1Testing a Hypothesis about a Coefficient 1555.4.2The F Statistic and the Least Squares Discrepancy 157Testing Restrictions Using the Fit of the Regression1615.5.15.5.25.5.35.5.45.95.10The Restricted Least Squares Estimator 161The Loss of Fit from Restricted Least Squares 162Testing the Significance of the Regression 166Solving Out the Restrictions and a Caution aboutUsing R2 166Nonnormal Disturbances and Large-Sample Tests167Testing Nonlinear Restrictions171Choosing between Nonnested Models1745.8.1Testing Nonnested Hypotheses 1745.8.2An Encompassing Model 1755.8.3Comprehensive Approach—The J Test 176A Specification Test177Model Building—A General to Simple Strategy1785.115.10.1Model Selection Criteria 1795.10.2Model Selection 1805.10.3Classical Model Selection 1805.10.4Bayesian Model Averaging 181Summary and Conclusions1835.65.75.8
Greene-2140242A01 GREE3568 07 GE FMJanuary 19, 201120:15ContentsCHAPTER 6 Functional Form and Structural Change6.1Introduction1896.2Using Binary inary Variables in Regression 189Several Categories 192Several Groupings 192Threshold Effects and Categorical Variables 194Treatment Effects and Differences in DifferencesRegression 195Nonlinearity in the Variables1986.3.1Piecewise Linear Regression 1986.3.2Functional Forms 2006.3.3Interaction Effects 2016.3.4Identifying Nonlinearity 2026.3.5Intrinsically Linear Models 205Modeling and Testing for a Structural Break2086.4.1Different Parameter Vectors 2086.4.2Insufficient Observations 2096.4.3Change in a Subset of Coefficients 2106.4.4Tests of Structural Break with Unequal Variances 2116.4.5Predictive Test of Model Stability 214Summary and Conclusions215CHAPTER 77.17.27.37.47.57.6Nonlinear, Semiparametric, and NonparametricRegression Models221Introduction221Nonlinear Regression Models2227.2.1Assumptions of the Nonlinear Regression Model 2227.2.2The Nonlinear Least Squares Estimator 2247.2.3Large Sample Properties of the Nonlinear Least SquaresEstimator 2267.2.4Hypothesis Testing and Parametric Restrictions 2297.2.5Applications 2317.2.6Computing the Nonlinear Least Squares Estimator 240Median and Quantile Regression2427.3.1Least Absolute Deviations Estimation 2437.3.2Quantile Regression Models 247Partially Linear Regression250Nonparametric Regression252Summary and Conclusions255CHAPTER 8 Endogeneity and Instrumental Variable Estimation8.1Introduction2598.2Assumptions of the Extended Model2632599
Greene-2140242A01 GREE3568 07 GE FM10January 19, 201120:15Contents8.38.4Estimation2648.3.1Least Squares 2658.3.2The Instrumental Variables Estimator 2658.3.3Motivating the Instrumental Variables Estimator8.3.4Two-Stage Least Squares 270Two Specification Tests2738.58.4.1The Hausman and Wu Specification Tests8.4.2A Test for Overidentification 278Measurement Error2798.68.5.1Least Squares Attenuation 2808.5.2Instrumental Variables Estimation 2828.5.3Proxy Variables 282Nonlinear Instrumental Variables Estimation2868.7Weak Instruments8.88.9Natural Experiments and the Search for Causal EffectsSummary and Conclusions294PART II267274289291Generalized Regression Model and Equation SystemsCHAPTER 9 The Generalized Regression Model and Heteroscedasticity9.1Introduction2979.2Inefficient Estimation by Least Squares and -Sample Properties of Ordinary Least Squares 2999.2.2Asymptotic Properties of Ordinary Least Squares 2999.2.3Robust Estimation of Asymptotic Covariance Matrices 3019.2.4Instrumental Variable Estimation 302Efficient Estimation by Generalized Least Squares3049.3.1Generalized Least Squares (GLS) 3049.3.2Feasible Generalized Least Squares (FGLS) 306Heteroscedasticity and Weighted Least Squares3089.4.1Ordinary Least Squares Estimation 3099.4.2Inefficiency of Ordinary Least Squares 3109.4.3The Estimated Covariance Matrix of b 3109.4.4Estimating the Appropriate Covariance Matrix for OrdinaryLeast Squares 312Testing for Heteroscedasticity3159.5.1White’s General Test 3159.5.2The Breusch–Pagan/Godfrey LM Test 316Weighted Least Squares3179.6.1Weighted Least Squares with Known 3189.6.2Estimation When Contains Unknown Parameters 319
Greene-2140242A01 GREE3568 07 GE FMJanuary 19, icative Heteroscedasticity 3209.7.2Groupwise Heteroscedasticity 322Summary and Conclusions325CHAPTER 10 Systems of Equations10.1 Introduction33010.21133010.3The Seemingly Unrelated Regressions Model33210.2.1Generalized Least Squares 33310.2.2Seemingly Unrelated Regressions with Identical Regressors10.2.3Feasible Generalized Least Squares 33610.2.4Testing Hypotheses 33610.2.5A Specification Test for the SUR Model 33710.2.6The Pooled Model 339Seemingly Unrelated Generalized Regression Models34410.4Nonlinear Systems of Equations10.5Systems of Demand Equations: Singular Systems34710.5.1Cobb–Douglas Cost Function 34710.5.2Flexible Functional Forms: The Translog Cost Function 350Simultaneous Equations Models35410.6.1Systems of Equations 35510.6.2A General Notation for Linear Simultaneous EquationsModels 35810.6.3The Problem of Identification 36110.6.4Single Equation Estimation and Inference 36610.6.5System Methods of Estimation 36910.6.6Testing in the Presence of Weak Instruments 374Summary and Conclusions37610.610.7335345CHAPTER 11 Models for Panel Data38311.1 Introduction38311.2 Panel Data Models38411.2.1General Modeling Framework for Analyzing Panel Data11.2.2Model Structures 38611.2.3Extensions 38711.2.4Balanced and Unbalanced Panels 38811.2.5Well-Behaved Panel Data 38811.3 The Pooled Regression Model38911.3.1Least Squares Estimation of the Pooled Model 38911.3.2Robust Covariance Matrix Estimation 39011.3.3Clustering and Stratification 39211.3.4Robust Estimation Using Group Means 394385
Greene-2140242A01 GREE3568 07 GE FM12January 19, ion with First Differences 39511.3.6The Within- and Between-Groups Estimators 397The Fixed Effects Model39911.4.1Least Squares Estimation 40011.4.2Small T Asymptotics 40211.4.3Testing the Significance of the Group Effects 40311.4.4Fixed Time and Group Effects 40311.4.5Time-Invariant Variables and Fixed Effects VectorDecomposition 404Random Effects41011.5.1Least Squares Estimation 41211.5.2Generalized Least Squares 41311.5.3Feasible Generalized Least Squares When Is Unknown 41411.5.4Testing for Random Effects 41611.5.5Hausman’s Specification Test for the Random EffectsModel 41911.5.6Extending the Unobserved Effects Model: Mundlak’sApproach 42011.5.7Extending the Random and Fixed Effects Models:Chamberlain’s Approach 421Nonspherical Disturbances and Robust Covariance Estimation42511.6.1Robust Estimation of the Fixed Effects Model 42511.6.2Heteroscedasticity in the Random Effects Model 42711.6.3Autocorrelation in Panel Data Models 42811.6.4Cluster (and Panel) Robust Covariance Matrices for Fixed andRandom Effects Estimators 428Spatial Autocorrelation429Endogeneity43411.8.1Hausman and Taylor’s Instrumental Variables Estimator 43411.8.2Consistent Estimation of Dynamic Panel Data Models:Anderson and Hsiao’s IV Estimator 43811.8.3Efficient Estimation of Dynamic Panel Data Models—TheArellano/Bond Estimators 44011.8.4Nonstationary Data and Panel Data Models 45011.9 Nonlinear Regression with Panel Data45111.9.1A Robust Covariance Matrix for Nonlinear Least Squares 45111.9.2Fixed Effects 45211.9.3Random Effects 45411.10 Systems of Equations45511.11 Parameter Heterogeneity45611.11.1 The Random Coefficients Model 45711.11.2 A Hierarchical Linear Model 46011.11.3 Parameter Heterogeneity and Dynamic Panel DataModels 46111.12 Summary and Conclusions466
Greene-2140242A01 GREE3568 07 GE FMJanuary 19, 201120:15ContentsPART IIIEstimation MethodologyCHAPTER 12 Estimation Frameworks in Econometrics12.1 Introduction47212.2 Parametric Estimation and Inference47412.312.412.547212.2.1Classical Likelihood-Based Estimation 47412.2.2Modeling Joint Distributions with Copula Functions 476Semiparametric Estimation47912.3.1GMM Estimation in Econometrics 47912.3.2Maximum Empirical Likelihood Estimation 48012.3.3Least Absolute Deviations Estimation and QuantileRegression 48112.3.4Kernel Density Methods 48212.3.5Comparing Parametric and Semiparametric Analyses 483Nonparametric Estimation48412.4.1Kernel Density Estimation 485Properties of Estimators48712.5.112.5.212.5.312.6Statistical Properties of Estimators 488Extremum Estimators 489Assumptions for Asymptotic Properties of ExtremumEstimators 48912.5.4Asymptotic Properties of Estimators 49212.5.5Testing Hypotheses 493Summary and Conclusions494CHAPTER 13 Minimum Distance Estimation and the GeneralizedMethod of Moments49513.1 Introduction49513.213.313.413.5Consistent Estimation: The Method of Moments49613.2.1Random Sampling and Estimating the Parameters ofDistributions 49713.2.2Asymptotic Properties of the Method of MomentsEstimator 50113.2.3Summary—The Method of Moments 503Minimum Distance Estimation503The Generalized Method of Moments (GMM) Estimator50813.4.1Estimation Based on Orthogonality Conditions 50813.4.2Generalizing the Method of Moments 51013.4.3Properties of the GMM Estimator 514Testing Hypotheses in the GMM Framework51913.5.1Testing the Validity of the Moment Restrictions 51913.5.2GMM Counterparts to the WALD, LM, and LRTests 52013
Greene-2140242A01 GREE3568 07 GE FM14January 19, 201120:15Contents13.613.7GMM Estimation of Econometric Models52213.6.1Single-Equation Linear Models 52213.6.2Single-Equation Nonlinear Models 52813.6.3Seemingly Unrelated Regression Models 53113.6.4Simultaneous Equations Models with Heteroscedasticity 53313.6.5GMM Estimation of Dynamic Panel Data Models 536Summary and Conclusions547CHAPTER 14 Maximum Likelihood Estimation14.1 Introduction54954914.214.3The Likelihood Function and Identification of the ParametersEfficient Estimation: The Principle of Maximum Likelihood14.4Properties of Maximum Likelihood Estimators55314.4.1Regularity Conditions 55414.4.2Properties of Regular Densities 55514.4.3The Likelihood Equation 55714.4.4The Information Matrix Equality 55714.4.5Asymptotic Properties of the Maximum LikelihoodEstimator 55714.4.5.a Consistency 55814.4.5.b Asymptotic Normality 55914.4.5.c Asymptotic Efficiency 56014.4.5.d Invariance 56114.4.5.e Conclusion 56114.4.6Estimating the Asymptotic Variance of the MaximumLikelihood Estimator 561Conditional Likelihoods, Econometric Models, and the GMMEstimator563Hypothesis and Specification Tests and Fit Measures56414.6.1The Likelihood Ratio Test 56614.6.2The Wald Test 56714.6.3The Lagrange Multiplier Test 56914.6.4An Application of the Likelihood-Based Test Procedures14.6.5Comparing Models and Computing Model Fit 57314.6.6Vuong’s Test and the Kullback–Leibler InformationCriterion 574Two-Step Maximum Likelihood Estimation576Pseudo-Maximum Likelihood Estimation and Robust AsymptoticCovariance Matrices58214.8.1Maximum Likelihood and GMM Estimation 58314.8.2Maximum Likelihood and M Estimation 58314.8.3Sandwich Estimators 58514.8.4Cluster Estimators 58614.514.614.714.8549551571
Greene-2140242A01 GREE3568 07 GE FMJanuary 19, 201120:15Contents1514.9Applications of Maximum Likelihood Estimation58814.9.1The Normal Linear Regression Model 58814.9.2The Generalized Regression Model 59214.9.2.a Multiplicative Heteroscedasticity 59414.9.2.b Autocorrelation 59714.9.3Seemingly Unrelated Regression Models 60014.9.3.a The Pooled Model 60014.9.3.b The SUR Model 60214.9.3.c Exclusion Restrictions 60214.9.4Simultaneous Equations Models 60714.9.5Maximum Likelihood Estimation of Nonlinear RegressionModels 60814.9.6Panel Data Applications 61314.9.6.a ML Estimation of the Linear Random EffectsModel 61414.9.6.b Nested Random Effects 61614.9.6.c Random Effects in Nonlinear Models: MLE UsingQuadrature 62014.9.6.d Fixed Effects in Nonlinear Models: Full MLE 62414.10 Latent Class and Finite Mixture Models62814.10.1 A Finite Mixture Model 62914.10.2 Measured and Unmeasured Heterogeneity14.10.3 Predicting Class Membership 63114.10.4 A Conditional Latent Class Model 63214.10.5 Determining the Number of Classes 63414.10.6 A Panel Data Application 63514.11 Summary and Conclusions638631CHAPTER 15 Simulation-Based Estimation and Inference and Random ParameterModels64315.1 Introduction64315.2 Random Number Generation64515.315.415.515.615.2.1Generating Pseudo-Random Numbers 64515.2.2Sampling from a Standard Uniform Population 64615.2.3Sampling from Continuous Distributions 64715.2.4Sampling from a Multivariate Normal Population 64815.2.5Sampling from Discrete Populations 648Simulation-Based Statistical Inference: The Method of Krinsky andRobb649Bootstrapping Standard Errors and Confidence Intervals651Monte Carlo Studies65515.5.1A Monte Carlo Study: Behavior of a Test Statistic 65715.5.2A Monte Carlo Study: The Incidental Parameters ProblemSimulation-Based Estimation66115.6.1Random Effects in a Nonlinear Model 661659
Greene-2140242A01 GREE3568 07 GE FM16January 19, 201120:15Contents15.6.215.715.815.9Monte Carlo Integration 66315.6.2.a Halton Sequences and Random Draws forSimulation-Based Integration 66515.6.2.b Computing Multivariate Normal Probabilities Usingthe GHK Simulator 66715.6.3Simulation-Based Estimation of Random Effects Models 669A Random Parameters Linear Regression Model674Hierarchical Linear Models679Nonlinear Random Parameter Models68115.10 Individual Parameter Estimates68215.11 Mixed Models and Latent Class Models15.12 Summary and ConclusionsCHAPTER 16 Bayesian Estimation and Inference16.1 Introduction69516.2 Bayes Theorem and the Posterior Density16.369069369569616.5Bayesian Analysis of the Classical Regression Model16.3.1Analysis with a Noninformative Prior 69916.3.2Estimation with an Informative Prior DensityBayesian Inference70416.4.1Point Estimation 70416.4.2Interval Estimation 70516.4.3Hypothesis Testing 70616.4.4Large-Sample Results 708Posterior Distributions and the Gibbs Sampler70816.616.716.8Application: Binomial Probit Model711Panel Data Application: Individual Effects Models714Hierarchical Bayes Estimation of a Random Parameters Model16.9Summary and Conclusions16.4PART IV698701718Cross Sections, Panel Data, and MicroeconometricsCHAPTER 17 Discrete Choice72117.1 Introduction72117.2 Models for Binary Outcomes72317.2.1Random Utility Models for Individual Choice 72417.2.2A Latent Regression Model 72617.2.3Functional Form and Regression 72717.3 Estimation and Inference in Binary Choice Models73017.3.1Robust Covariance Matrix Estimation 73217.3.2Marginal Effects and Average Partial Effects 733716
Greene-2140242A01 GREE3568 07 GE FMJanuary 19, 201120:15Contents17.417.517.3.2.a Average Partial Effects 73617.3.2.b Interaction Effects 73917.3.3Measuring Goodness of Fit 74117.3.4Hypothesis Tests 74317.3.5Endogenous Right-Hand-Side Variables in Binary ChoiceModels 74617.3.6Endogenous Choice-Based Sampling 75017.3.7Specification Analysis 75117.3.7.a Omitted Variables 75317.3.7.b Heteroscedasticity 754Binary Choice Models for Panel Data75617.4.1The Pooled Estimator 75717.4.2Random Effects Models 75817.4.3Fixed Effects Models 76117.4.4A Conditional Fixed Effects Estimator 76217.4.5Mundlak’s Approach, Variable Addition, and BiasReduction 76717.4.6Dynamic Binary Choice Models 76917.4.7A Semiparametric Model for Individual Heterogeneity 77117.4.8Modeling Parameter Heterogeneity 77317.4.9Nonresponse, Attrition, and Inverse Probability Weighting 774Bivariate and Multivariate Probit um Likelihood Estimation 779Testing for Zero Correlation 782Partial Effects 782A Panel Data Model for Bivariate Binary Response 784Endogenous Binary Variable in a Recursive Bivariate ProbitModel 78517.5.6Endogenous Sampling in a Binary Choice Model 78917.5.7A Multivariate Probit Model 792Summary and Conclusions795CHAPTER 18 Discrete Choices and Event Counts18.1 Introduction80018.2 Models for Unordered Multiple 8.2.8800801Random Utility Basis of the Multinomial Logit ModelThe Multinomial Logit Model 803The Conditional Logit Model 806The Independence from Irrelevant AlternativesAssumption 807Nested Logit Models 808The Multinomial Probit Model 810The Mixed Logit Model 811A Generalized Mixed Logit Model 812801
Greene-2140242A01 GREE3568 07 GE FM18January 19, 201120:15Contents18.2.918.318.418.5Application: Conditional Logit Model for Travel ModeChoice 81318.2.10 Estimating Willingness to Pay 81918.2.11 Panel Data and Stated Choice Experiments 82118.2.12 Aggregate Market Share Data—The BLP Random ParametersModel 822Random Utility Models for Ordered Choices82418.3.1The Ordered Probit Model 82718.3.2A Specification Test for the Ordered Choice Model 83118.3.3Bivariate Ordered Probit Models 83218.3.4Panel Data Applications 83418.3.4.a Ordered Probit Models with Fixed Effects 83418.3.4.b Ordered Probit Models with Random Effects 83518.3.5Extensions of the Ordered Probit Model 83818.3.5.a Threshold Models—Generalized Ordered ChoiceModels 83918.3.5.b Thresholds and Heterogeneity—AnchoringVignettes 840Models for Counts of Events84218.4.1The Poisson Regression Model 84318.4.2Measuring Goodness of Fit 84418.4.3Testing for Overdispersion 84518.4.4Heterogeneity and the Negative Binomial RegressionMode
Klein Mathematical Methods for Economics Krugman/Obstfeld/Melitz International Economics: Theory & Policy* Laidler The Demand for Money Leeds/von Allmen The Economics of Sports Leeds/von Allmen/Schiming Economics* Lipsey/Ragan/Storer Economics* Lynn Economic Development: Theory and Practice for a Divided World
SPATIAL ECONOMETRIC MODELLING OF MASSIVE DATASETS: THE CON-TRIBUTION OF DATA MINING G. Arbia, 1 M. Tabasso 2 Abstract In this paper we provide a brief overview of some of the most recent empirical research on spatial econometric models and spatial data mining. Data mining in general is t
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Solutions and Applications Manual Econometric Analysis . Prentice Hall, Upper Saddle River, New Jersey 07458 . Contents and Notation This book presents solutions to the end of chapter exercises and applications in Econometric Analysis. There are no exercises in the text for Appendices A – E. For the instructor or student who is interested .
Most econometric theory adapts methods originally developed in statistics. The major exception to this rule is the econometric analysis of the identification problem and the companion analyses of structural equations, causality, and economic policy evaluation. [Heckman 2000, p. 45, emphasis added.] . . .
Ph. D Student at University of Lome, Togo E-mail: kebalo.leleng@hotmail.fr Abstract This paper is an econometric investigation, an analysis on the difficulty of modeling the south african exchange rate. The aim of our paper is to examine the nature of the existing relationship between the real exchange rate of the Rand, real prices of gold .
Pagan’s complaint still holds – the evidence we found is overly limited and sometimes contradictory, which emphasises the need for research centred around establishing operational principles of econometric model building and delineating the more limited role of tacit knowledge. The need for principles in econometric forecasting
holds – the evidence we found is overly limited and sometimes contradictory, which emphasises the need for research centered around establishing operational principles of econometric model building and delineating the more limited role of tacit knowledge. The need for principles in econometric forecasting
