Econometric Analysis Of Cross Section And Panel Data/ (Registro n. 2229)

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Campo fixo de controle local 190614s1960 -us gr 000 0 us u
020 ## - ISBN
ISBN 262232197
040 ## - Fonte da Catalogação
Fonte de catalogação BR-BrCADE
090 ## - Número de Chamada
Localização na estante 330.015195 W913e
Cutter W913e
100 10 - Autor
Autor WOOLDRIDGE, Jeffrey M.
245 10 - Titulo Principal
Título principal Econometric Analysis Of Cross Section And Panel Data/
260 ## - Local, Editora e Data
Cidade Estados Unidos:
Editora MIT Press,
Data 1960.
300 ## - Descrição Física
Número de páginas 752 p.
505 ## - Conteúdo
Conteúdo Preface <br/>Acknowledgments <br/><br/>1 INTRODUCTION AND BACKGROUND<br/>1 Introduction <br/>1.1 Causal Relationships and Cetens Paribus Analysis <br/>1.2 The Stochastic Setting and Asymptotic Analysis <br/>1.2.1 Data Structures <br/>1.2.2 Asymptotic Analysis <br/>1.3 Some Examples <br/>1.4 Why Not Fixed Explanatory Variables? <br/><br/>2 Conditional Expectations and Related Concepts in Econometrics <br/>2.1 The Role of Conditional Expectations in Econometrics <br/>2.2 Features of Conditional Expectations <br/>2.2.1 Definition and Examples <br/>2.2.2 Partial Effects, Elasticities, and Semielasticities <br/>2.2.3 The Error Form of Models of Conditional Expectations <br/>2.2.4 Some Properties of Conditional Expectations <br/>2.2.5 Average Partial Effects <br/>2.3 Linear Projections<br/>Problems<br/>Appendix 2A<br/>2.A.1 Properties of Conditional Expectations <br/>2.A.2 Properties of Conditional Variances <br/>2.A.3 Properties of Linear Projections<br/><br/>3 Basic Asymptotic Theory<br/>3.1 Convergence of Deterministic Sequences<br/>3.2 Convergence in Probability and Bounded in Probability<br/>3.3 Convergence in Distribution<br/>3.4 Limit Theorems for Random Samples<br/>3.5 Luniting Behavior of Estimators and Test Statistics <br/>3.5.1 Asymptotic Properties of Estimators <br/>3.5.2 Asymptotic Properties of Test Statistics <br/>Problems<br/><br/>II LINEAR MODELS<br/>4 The Single-Equation Linear Model and OLS Estimation <br/>4.1 Overview of the Single-Equation Linear Model <br/>4.2 Asymptotic Properties of OLS <br/>4.2.1 Consistency <br/>4.2.2 Asymptotic Inference Using OLS <br/>4.2.3 Heteroskedasticity-Robust Inference <br/>4.2.4 Lagrange Multiplier (Score) Tests <br/>4.3 OLS Solutions to the Omitted Variables Problem <br/>4.3.1 OLS Ignoring the Omitted Variables <br/>4.3.2 The Proxy Variable—OLS Solution <br/>4.3.3 Modeis with Interactions in Unobservables <br/>4.4 Properties of OLS under Measurement Error <br/>4.4.1 Measurement Error in the Dependent Variable <br/>4.4.2 Measurement Error in an Explanatory Variable <br/>Problems <br/><br/>5 Instrumental Variables Estimation of Single-Equation Linear Modeis <br/>5.1 Instrumental Variables and Two-Stage Least Squares <br/>5.1.1 Motivation for Instrumental Variables Estimation<br/>5.1.2 Multiple Instruments: Two-Stage Least Squares <br/>5.2 General Treatment of 2SLS <br/>5.2.1 Consistency <br/>5.2.2 Asymptotic Normality of 2SLS <br/>5.2.3 Asymptotic Efficiency of 2SLS <br/>5.2.4 Hypothesis Testing with 2SLS<br/>5.2.5 Heteroskedasticity-Robust Inference for 2SLS <br/>5.2.6 Potential Pitfalls with 2SLS <br/>5.3 IV Solutions to the Omitted Variables and Measurement Error <br/>Problems <br/>5.3.1 Leaving the Omitted Factors in the Error Term <br/>5.3.2 Solutions Using Indicators of the Unobservable <br/>Problems <br/><br/>6 Additional Single-Equation Topics <br/>6.1 Estimation with Generated Regressors and Instruments <br/>6.1.1 OLS with Generated Regressors <br/>6.1.2 2SLS with Generated Instruments <br/>6.1.3 Generated Instruments and Regressors <br/>6.2 Some Specification Tests <br/>6.2.1 Testing for Endogeneity<br/>6.2.2 Testing Overidentifying Restrictions <br/>6.2.3 Testing Functional Form <br/>6.2.4 Testing for Heteroskedasticity<br/>6.3 Single-Equation Methods under Other Sampling Schemes <br/>6.3.1 Pooled Cross Sections over Time <br/>6.3.2 Geographically Stratified Samples <br/>6.3.3 Spatial Dependence <br/>6.3.4 Cluster Samples <br/>Problems <br/>Appendix 6A <br/><br/>7 Estimating Systems of Equations by OLS and GLS <br/>7.1 Introduction <br/>7.2 Some Examples <br/>7.3 System OLS Estimation of a Multivariate Linear System<br/>7.3.1 Preliminaries <br/>7.3.2 Asymptotic Properties of System OLS <br/>7.3.3 Testing Multiple Hypotheses <br/>7.4 Consistency and Asymptotic Normality of Generalized Least Squares <br/>7.4.1 Consistency<br/>7.4.2 Asymptotic Normality <br/>7.5 Feasible GLS <br/>7.5.1 Asymptotic Properties <br/>7.5.2 Asymptotic Variance of FGLS under a Standard Assumption <br/>7.6 Testing Using FGLS <br/>7.7 Seemingly Unrelated Regressions, Revisited <br/>7.7.1 Comparison between OLS and FGLS for SUR Systems <br/>7.7.2 Systems with Cross Equation Restrictions <br/>7.7.3 Singular Variance Matrices in SUR Systems <br/>7.8 The Linear Panei Data Model, Revisited <br/>7.8.1 Assumptions for Pooled OLS <br/>7.8.2 Dynamic Compieteness <br/>7.8.3 A Note on Time Series Persistence <br/>7.8.4 Robust Asymptotic Variance Matrix <br/>7.8.5 Testing for Serial Correlation and Heteroskedasticity after Pooled OLS <br/>7.8.6 Feasibie GLS Estimation under Strict Exogeneity <br/>Problems <br/><br/>8 System Estimation by Instrumental Variables <br/>8.1 Introduction and Exampies <br/>8.2 A General Linear System of Equations <br/>8.3 Generalized Method of Moments Estimation <br/>8.3.1 A General Weighting Matrix <br/>8.3.2 The System 2SLS Estimator<br/>8.3.3 The Optimal Weighting Matrix <br/>8.3.4 The Three-Stage Least Squares Estimator<br/>8.3.5 Comparison between GMM 3SLS and Traditional 3SLS<br/>8.4 Some Considerations When Choosing an Estimator<br/>8.5 Testing Using GMM <br/>8.5.1 Testing Classical Hypotheses <br/>8.5.2 Testing Overidentification Restrictions <br/>8.6 More Efficient Estimation and Optimai Instruments <br/>Problems <br/><br/>9 Siinultaneous Equations Modeis <br/>9.1 The Scope of Simultaneous Equations Modeis <br/>9.2 Identiflcation in a Linear System <br/>9.2.1 Exclusion Restrictions and Reduced Forms <br/>9.2.2 General Linear Restrictions and Structural Equations <br/>9.2.3 Unidentified, Just Identified, and Overidentified Equations <br/>9.3 Estimation after Identification <br/>9.3.1 The Robustness-Efficiency Trade-off<br/>9.3.2 When Are 2SLS and 3SLS Equivalent?<br/>9.3.3 Estimating the Reduced Form Parameters <br/>9.4 Additional Topics in Linear SEMs <br/>9.4.1 Using Cross Equation Restrictions to Achieve Identification <br/>9.4.2 Using Covariance Restrictions to Achieve Identification <br/>9.4.3 Subtieties Concerning Identification and Efficiency in Linear <br/>Systems <br/>9.5 SEMs Nonlinear in Endogenous Variables <br/>9.5.1 Identification <br/>9.5.2 Estimation <br/>9.6 Different Instruments for Different Equations <br/>Probiems <br/><br/>10 Basic Linear Unobserved Effects Panei Data Modeis<br/>10.1 Motivation: The Omitted Variables Problem <br/>10.2 Assumptions about the Unobserved Effects and Explanatory Variables <br/>10.2.1 Random or Fixed Effects? <br/>10.2.2 Strict Exogeneity Assumptions on the Explanatory Variables<br/>10.2.3 Some Examples of Unobserved Effects Panei Data Modeis <br/>10.3 Estimating Unobserved Effects Modeis by Pooled OLS <br/>10.4 Random Effects Methods <br/>10.4.1 Estimation and Inference under the Basic Random Effects Assumptions <br/>10.4.2 Robust Variance Matrix Estimator <br/>10.4.3 A General FGLS Analysis <br/>10.4.4 Testing for the Presence of an Unobserved Effect<br/>10.5 Fixed Effects Methods <br/>10.5.1 Consistency of the Fixed Effects Estimator <br/>10.5.2 Asymptotic Inference with Fixed Effects <br/>10.5.3 The Dummy Variable Regression <br/>10.5.4 Serial Correlation and the Robust Variance Matrix Estimator <br/>10.5.5 Fixed Effects GLS <br/>10.5.6 Using Fixed Effects Estimation for Policy Analysis <br/>10.6 First Differencing Methods <br/>10.6.1 Inference <br/>10.6.2 Robust Variance Matrix <br/>10.6.3 Testing for Serial Correlation <br/>10.6.4 Poiicy Analysis Using First Differencing<br/>10.7 Comparison of Estimators<br/>10.7.1 Fixed Effects versus First Differencing <br/>10.7.2 The Relationship between the Random Effects and Fixed Effects Estimators <br/>10.7.3 The Hausman Test Comparing the RE and FE Estimators <br/>Problems<br/><br/>11 More Topics m Linear Unobserved Effects Modeis <br/>11.1 Unobserved Effects Modeis without the Strict Exogeneity Assumption <br/>11.1.1 Models under Sequential Moment Restrictio<br/>11.1.2 Models with Strictly and Sequentially Exogenous Explanatory Variables <br/>11.1.3 Modeis with Contemporaneous Correlation between Some Explanatory Variables and the Idiosyncratic Error <br/>11.1.4 Summary of Modeis without Strictly Exogenous Explanatory Variables <br/>11.2 Modeis with Individual-Specific Siopes <br/>11.2.1 A Random Trend Model <br/>11.2.2 General Modeis with Individual-Specific Siopes <br/>11.3 GMM Approaches to Linear Unobserved Effects Models <br/>11.3.1 Equivalence between 3SLS and Standard Panei Data Estimators <br/>11.3.2 Chamberlain's Approach to Unobserved Effects Models <br/>11.4 Hausman and Taylor-Type Models <br/>11.5 Applying Panel Data Methods to Matched Pairs and Cluster Samples <br/>Problems <br/><br/>III GENERAL APPROACHES TO ~LINEAR ESTIMÁTION <br/>12 M-Estimation <br/>12.1 Introduction<br/>12.2 Identification, Uniform Convergence, and Consistency<br/>12.3 Asymptotic Normality <br/>12.4 Two-Step M-Estimators<br/>12.4.1 Consistency <br/>12.4.2 Asymptotic Normality <br/>12.5 Estimating the Asymptotic Variance <br/>12.5.1 Estimation without Nuisance Parameters <br/>12.5.2 Adjustments for Two-Step Estimation <br/>12.6 Hypothesis Testing <br/>12.6.1 Wald Tests <br/>12.6.2 Score (or Lagrange Multiplier) Tests <br/>12.6.3 Tests Based on the Change in the Objective Function <br/>12.6.4 Behavior of the Statistics under Alternatives <br/>12.7 Optimization Methods <br/>12.7.1 The Newton-Raphson Method <br/>12.7.2 The Berndt, Hall, Hall, and Hausman Algorithm <br/>12.7.3 The Generalized Gauss-Newton Method <br/>12.7.4 Concentrating Parameters out of the Objective Function <br/>12.8 Simulation and Resampling Methods <br/>12.8.1 Monte Carlo Simulation <br/>12.8.2 Bootstrapping <br/>Problems <br/><br/>13 Maximum Likelihood Methods<br/>13.1 Introduction <br/>13.2 Preliminaries and Examples <br/>13.3 General Framework for Conditional MLE <br/>13.4 Consistency of Conditional MLE <br/>13.5 Asymptotic Normality and Asymptotic Variance Estimation <br/>13.5.1 Asymptotic Normality <br/>13.5.2 Estimating the Asymptotic Variance<br/>13.6 Hypothesis Testing <br/>13.7 Specilication Testing <br/>13.8 Partial Likelihood Methods for Panel Data and Cluster Samples <br/>13.8.1 Setup for Panel Data <br/>13.8.2 Asymptotic Inference <br/>13.8.3 Inference with Dynamically Complete Models <br/>13.8.4 Inference under Cluster Sampling <br/>13.9 Panel Data Modeis with Unobserved Effects <br/>13.9.1 Modeis with Strictiy Exogenous Explanatory Variables <br/>13.9.2 Modeis with Lagged Dependent Variables <br/>13.10 Two-StepMLE <br/>Problems <br/>Appendix 13A <br/><br/>14 Generalized Method of Moments and Minimum Distance Estimation <br/>14.1 Asymptotic Properties ofGMM <br/>14.2 Estimation under Orthogonaiity Conditions <br/>14.3 Systems of Noniinear Equations <br/>14.4 Panei Data Applications <br/>14.5 Efficient Estimation <br/>14.5.1 A General Efficiency Framework <br/>14.5.2 Efficiency of MLE <br/>14.5.3 Efficient Choice of Instruments under Conditional Moment Restrictions <br/>14.6 Classical Minimum Distance Estimation<br/>Problems <br/>Appendix 14A <br/><br/>IV LINEAR MODELS AND RELATED TOPICS<br/>15 Discrete Response Models <br/>15.1 Introduction <br/>15.2 The Linear Probability Model for Binary Response <br/>15.3 Index Models for Binary Response: Probit and Logit <br/>15.4 Maximum Likelihood Estimation of Binary Response Index Models <br/>15.5Testing m Binary Response Index Modeis <br/>15.5.1 Testing Multipie Exclusion Restrictions <br/>15.5.2 Testing Nonlinear Hypotheses about <br/>15.5.3 Tests against More General Alternatives <br/>15.6 Reporting the Results for Probit and Logit<br/>15.7 Specification Issues in Binary Response Models <br/>15.7.1 Neglected Heterogeneity <br/>15.7.2 Continuous Endogenous Expianatory Variables <br/>15.7.3 A Binary Endogenous Explanatory Variable<br/>15.7.4 Heteroskedasticity and Nonnormality in the Latent Variable Model<br/>15.7.5 Estimation under Weaker Assumptions <br/>15.8 Binary Response Models for Panel Data and Cluster Samples <br/>15.8.1 Pooled Probit and Logit <br/>15.8.2 Unobserved Effects Probit Models under Strict Exogeneity <br/>15.8.3 Unobserved Effects Logit Models under Strict Exogeneity <br/>15.8.4 Dynamic Unobserved Effects Models <br/>15.8.5 Semiparametric Approaches <br/>15.8.6 Cluster Samples <br/>15.9 Multinomial Response Models <br/>15.9.1 Multinomial Logit<br/>15.9.2 Probabilistic Choice Models <br/>15.10 Ordered Response Models <br/>15.10.1 Ordered Logit and Ordered Probit <br/>15.10.2 Applying Ordered Probit to Interval-Coded Data <br/>Problems <br/>16 Comer Solution Outcomes and Censored Regression Models <br/>16.1 Introduction and Motivation <br/>16.2 Derivations of Expected Values <br/>16.3 Inconsistency of OLS <br/>16.4 Estimation and Inference with Censored Tobit <br/>16.5 Reporting the Results <br/>16.6 Specffication Issues in Tobit Models <br/>16.6.1 Neglected Heterogeneity <br/>16.6.2 Endogenous Explanatory Variables <br/>16.6.3 Heteroskedasticity and Nonnormality in the Latent Variable Model<br/>16.6.4 Estimation under Conditional Median Restrictions <br/>16.7 Some Alternatives to Censored Tobit for Comer Solution Outcomes <br/>16.8 Applying Censored Regression to Panel Data and Cluster Samples <br/>16.8.1 Pooled Tobit <br/>16.8.2 Unobserved Effects Tobit Models under Strict Exogeneity <br/>16.8.3 Dynamic Unobserved Effects Tobit Models <br/>Problems <br/>17 Sample Selection, Attrition, and Stratified Sampling <br/>17.1 Introduction <br/>17.2 When Can Sample Selection Be Ignored? <br/>17.2.1 Linear Models: OLS and 2SLS <br/>17.2.2 Nonlinear Models <br/>17.3 Selection on the Basis of the Response Variable: Truncated Regression <br/>17.4 A Probit Selection Equation <br/>17.4.1 Exogenous Explanatory Variables <br/>17.4.2 Endogenous Explanatory Variables <br/>17.4.3 Binary Response Model with Sample Selection <br/>17.5 A Tobit Selection Equation <br/>17.5.1 Exogenous Explanatory Variables <br/>17.5.2 Endogenous Explanatory Variables <br/>17.6 Estimating Structural Tobit Equations with Sample Selection <br/>17.7 Sample Selection and Attrition in Linear Panel Data Models <br/>17.7.1 Fixed Effects Estimation with Unbalanced Panels <br/>17.7.2 Testing and Correcting for Sample Selection Bias <br/>17.7.3 Attention <br/>17.8 Stratified Sampling <br/>17.8.1 Standard Stratified Sampling and Variable Probability Sampling <br/>17.8.2 Weighted Estimators to Account for Stratification <br/>17.8.3 Stratification Based on Exogenous Variables <br/>Problems<br/>18 Estimating Average Treatment Effects <br/>18.1 Introduction <br/>18.2 A Counterfactual Setting and the Self-Selection Problem <br/>18.3 Methods Assuming Ignorability of Treatment <br/>18.3.1 Regression Methods <br/>18.3.2 Methods Based on the Propensity Score <br/>18.4 Instrumental Variables Methods<br/>18.4.1 Estimating the ATE Using IV<br/>18.4.2 Estimating the Local Average Treatment Effect by IV <br/>18.5 Further Issues<br/>18.5.1 Special Considerations for Binary and Comer Solution Responses <br/>18.5.2 Panel Data <br/>18.5.3 Nonbinary Treatments <br/>18.5.4 Multiple Treatments <br/>Problems <br/>19 Count Data and Related Models <br/>19.1 Why Count Data Models? <br/>19.2 Poisson Regression Models with Cross Section Data<br/>19.2.1 Assumptions Used for Poisson Regression <br/>19.2.2 Consistency of the Poisson QMLE <br/>19.2.3 Asymptotic Normality of the Poisson QMLE <br/>19.2.4 Hypothesis Testing <br/>19.2.5 Specification Testing <br/>19.3 Other Count Data Regression Models<br/>19.3.1 Negative Binomial Regression Models <br/>19.3.2 Binomial Regression Models <br/>19.4 Other QMLEs in the Linear Exponential Family <br/>19.4.1 Exponential Regression Models <br/>19.4.2 Fractional Logit Regression <br/>19.5 Endogeneity and Sample Selection with an Exponential Regression Function <br/>19.5.1 Endogeneity <br/>19.5.2 Sample Selection <br/>19.6 Panel Data Methods <br/>19.6.1 Pooled QMLE <br/>19.6.2 Specifying Models of Conditional Expectations with Unobserved Effects <br/>19.6.3 Random Effects Methods <br/>19.6.4 Fixed Effects Poisson Estimation <br/>19.6.5 Relaxing the Strict Exogeneity Assumption <br/>Problems <br/>20 Duration Analysis <br/>20.1 Introduction <br/>20.2 Hazard Functions <br/>20.2.1 Hazard Functions without Covariates <br/>20.2.2 Hazard Functions Conditional on Time-Invariant Covariates<br/>20.2.3 Hazard Functions Conditional on Time-Varying Covariates <br/>20.3 Analysis of Single-Spell Data with Time-Invariant Covariates<br/>20.3.1 Flow Sampling <br/>20.3.2 Maximum Likelihood Estimation with Censored Flow Data <br/>20.3.3 Stock Sampling <br/>20.3.4 Unobserved Heterogeneity <br/>20.4 Analysis of Grouped Duration Data <br/>20.4.1 Time-Invariant Covariates <br/>20.4.2 Time-Varying Covariates <br/>20.4.3 Unobserved Heterogeneity <br/>20.5 Further Issues <br/>20.5.1 Cox's Partia] Likelihood Method for the Proportional Hazard Model <br/>20.5.2 Multiple-Spell Data <br/>20.5.3 Competing Risks Modeis<br/>Problems <br/>References <br/>Index <br/><br/><br/>
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