a| 2010020912

a| 9780262232586 (hardcover : alk. paper)

a| 0262232588 (hardcover : alk. paper)

a| 3807714

a| ocn627701062

a| (OCoLC)627701062

a| DLC c| DLC d| C#P d| BWX d| YDXCP d| NLGGC d| CDX d| NhCcYME

a| JHEE a| JHSM

a| HB139 b| .W663 2010

a| 330.01/5195 2| 22

a| 83.03 2| bcl

a| Wooldridge, Jeffrey M., d| 1960-

a| Econometric analysis of cross section and panel data / c| Jeffrey M. Wooldridge.

a| 2nd ed.

a| Cambridge, Mass. : b| MIT Press, c| c2010.

a| xxvii, 1064 p. : b| ill. ; c| 24 cm.

a| Includes bibliographical references (p. [1025]-1044) and index.

g| I. t| INTRODUCTION AND BACKGROUND -- g| 1. t| Introduction -- g| 1.1. t| Causal Relationships and Ceteris Paribus Analysis -- g| 1.2. t| Stochastic Setting and Asymptotic Analysis -- g| 1.2.1. t| Data Structures -- g| 1.2.2. t| Asymptotic Analysis -- g| 1.3. t| Some Examples -- g| 1.4. t| Why Not Fixed Explanatory Variables? -- g| 2. t| Conditional Expectations and Related Concepts in Econometrics -- g| 2.1. t| Role of Conditional Expectations in Econometrics -- g| 2.2. t| Features of Conditional Expectations -- g| 2.2.1. t| Definition and Examples -- g| 2.2.2. t| Partial Effects, Elasticities, and Semielasticities -- g| 2.2.3. t| Error Form of Models of Conditional Expectations -- g| 2.2.4. t| Some Properties of Conditional Expectations -- g| 2.2.5. t| Average Partial Effects -- g| 2.3. t| Linear Projections -- t| Problems -- t| Appendix 2A -- g| 2.A.1. t| Properties of Conditional Expectations -- g| 2.A.2. t| Properties of Conditional Variances and Covariances -- g| 2.A.3. t| Properties of Linear Projections -- g| 3. t| Basic Asymptotic Theory -- g| 3.1. t| Convergence of Deterministic Sequences -- g| 3.2. t| Convergence in Probability and Boundedness in Probability -- g| 3.3. t| Convergence in Distribution -- g| 3.4. t| Limit Theorems for Random Samples -- g| 3.5. t| Limiting Behavior of Estimators and Test Statistics -- g| 3.5.1. t| Asymptotic Properties of Estimators -- g| 3.5.2. t| Asymptotic Properties of Test Statistics -- t| Problems -- g| II. t| LINEAR MODELS -- g| 4. t| Single-Equation Linear Model and Ordinary Least Squares Estimation -- g| 4.1. t| Overview of the Single-Equation Linear Model -- g| 4.2. t| Asymptotic Properties of Ordinary Least Squares -- g| 4.2.1. t| Consistency -- g| 4.2.2. t| Asymptotic Inference Using Ordinary Least Squares -- g| 4.2.3. t| Heteroskedasticity-Robust Inference -- g| 4.2.4. t| Lagrange Multiplier (Score) Tests -- g| 4.3. t| Ordinary Least Squares Solutions to the Omitted Variables Problem -- g| 4.3.1. t| Ordinary Least Squares Ignoring the Omitted Variables -- g| 4.3.2. t| Proxy Variable-Ordinary Least Squares Solution -- g| 4.3.3. t| Models with Interactions in Unobservables: Random Coefficient Models -- g| 4.4. t| Properties of Ordinary Least Squares under Measurement Error -- g| 4.4.1. t| Measurement Error in the Dependent Variable -- g| 4.4.2. t| Measurement Error in an Explanatory Variable -- t| Problems -- g| 5. t| Instrumental Variables Estimation of Single-Equation Linear Models -- g| 5.1. t| Instrumental Variables and Two-Stage Least Squares -- g| 5.1.1. t| Motivation for Instrumental Variables Estimation -- g| 5.1.2. t| Multiple Instruments: Two-Stage Least Squares -- g| 5.2. t| General Treatment of Two-Stage Least Squares -- g| 5.2.1. t| Consistency -- g| 5.2.2. t| Asymptotic Normality of Two-Stage Least Squares -- g| 5.2.3. t| Asymptotic Efficiency of Two-Stage Least Squares -- g| 5.2.4. t| Hypothesis Testing with Two-Stage Least Squares -- g| 5.2.5. t| Heteroskedasticity-Robust Inference for Two-Stage Least Squares -- g| 5.2.6. t| Potential Pitfalls with Two-Stage Least Squares -- g| 5.3. t| IV Solutions to the Omitted Variables and Measurement Error Problems -- g| 5.3.1. t| Leaving the Omitted Factors in the Error Term -- g| 5.3.2. t| Solutions Using Indicators of the Unobservables -- t| Problems -- g| 6. t| Additional Single-Equation Topics -- g| 6.1. t| Estimation with Generated Regressors and Instruments -- g| 6.1.1. t| Ordinary Least Squares with Generated Regressors -- g| 6.1.2. t| Two-Stage Least Squares with Generated Instruments -- g| 6.1.3. t| Generated Instruments and Regressors -- g| 6.2. t| Control Function Approach to Endogeneity -- g| 6.3. t| Some Specification Tests -- g| 6.3.1. t| Testing for Endogeneity -- g| 6.3.2. t| Testing Overidentifying Restrictions -- g| 6.3.3. t| Testing Functional Form -- g| 6.3.4. t| Testing for Heteroskedasticity -- g| 6.4. t| Correlated Random Coefficient Models -- g| 6.4.1. t| When Is the Usual IV Estimator Consistent? -- g| 6.4.2. t| Control Function Approach -- g| 6.5. t| Pooled Cross Sections and Difference-in-Differences Estimation -- g| 6.5.1. t| Pooled Cross Sections over Time -- g| 6.5.2. t| Policy Analysis and Difference-in-Differences Estimation -- t| Problems -- t| Appendix 6A -- g| 7. t| Estimating Systems of Equations by Ordinary Least Squares and Generalized Least Squares -- g| 7.1. t| Introduction -- g| 7.2. t| Some Examples -- g| 7.3. t| System Ordinary Least Squares Estimation of a Multivariate Linear System -- g| 7.3.1. t| Preliminaries -- g| 7.3.2. t| Asymptotic Properties of System Ordinary Least Squares -- g| 7.3.3. t| Testing Multiple Hypotheses -- g| 7.4. t| Consistency and Asymptotic Normality of Generalized Least Squares -- g| 7.4.1. t| Consistency -- g| 7.4.2. t| Asymptotic Normality -- g| 7.5. t| Feasible Generalized Least Squares -- g| 7.5.1. t| Asymptotic Properties -- g| 7.5.2. t| Asymptotic Variance of Feasible Generalized Least Squares under a Standard Assumption -- g| 7.5.3. t| Properties of Feasible Generalized Least Squares with (Possibly Incorrect) Restrictions on the Unconditional Variance Matrix -- g| 7.6. t| Testing the Use of Feasible Generalized Least Squares -- g| 7.7. t| Seemingly Unrelated Regressions, Revisited -- g| 7.7.1. t| Comparison between Ordinary Least Squares and Feasible Generalized Least Squares for Seemingly Unrelated Regressions Systems -- g| 7.7.2. t| Systems with Cross Equation Restrictions -- g| 7.7.3. t| Singular Variance Matrices in Seemingly Unrelated Regressions Systems -- g| 7.8. t| Linear Panel Data Model, Revisited -- g| 7.8.1. t| Assumptions for Pooled Ordinary Least Squares -- g| 7.8.2. t| Dynamic Completeness -- g| 7.8.3. t| Note on Time Series Persistence -- g| 7.8.4. t| Robust Asymptotic Variance Matrix -- g| 7.8.5. t| Testing for Serial Correlation and Heteroskedasticity after Pooled Ordinary Least Squares -- g| 7.8.6. t| Feasible Generalized Least Squares Estimation under Strict Exogeneity -- t| Problems -- g| 8. t| System Estimation by Instrumental Variables -- g| 8.1. t| Introduction and Examples -- g| 8.2. t| General Linear System of Equations -- g| 8.3. t| Generalized Method of Moments Estimation -- g| 8.3.1. t| General Weighting Matrix -- g| 8.3.2. t| System Two-Stage Least Squares Estimator -- g| 8.3.3. t| Optimal Weighting Matrix -- g| 8.3.4. t| Generalized Method of Moments Three-Stage Least Squares Estimator -- g| 8.4. t| Generalized Instrumental Variables Estimator -- g| 8.4.1. t| Derivation of the Generalized Instrumental Variables Estimator and Its Asymptotic Properties -- g| 8.4.2. t| Comparison of Generalized Method of Moment, Generalized Instrumental Variables, and the Traditional Three-Stage Least Squares Estimator -- g| 8.5. t| Testing Using Generalized Method of Moments -- g| 8.5.1. t| Testing Classical Hypotheses -- g| 8.5.2. t| Testing Overidentification Restrictions -- g| 8.6. t| More Efficient Estimation and Optimal Instruments -- g| 8.7. t| Summary Comments on Choosing an Estimator -- t| Problems -- g| 9. t| Simultaneous Equations Models -- g| 9.1. t| Scope of Simultaneous Equations Models -- g| 9.2. t| Identification in a Linear System -- g| 9.2.1. t| Exclusion Restrictions and Reduced Forms -- g| 9.2.2. t| General Linear Restrictions and Structural Equations -- g| 9.2.3. t| Unidentified, Just Identified, and Overidentified Equations -- g| 9.3. t| Estimation after Identification -- g| 9.3.1. t| Robustness-Efficiency Trade-off -- g| 9.3.2. t| When Are 2SLS and 3SLS Equivalent? -- g| 9.3.3. t| Estimating the Reduced Form Parameters -- g| 9.4. t| Additional Topics in Linear Simultaneous Equations Methods -- g| 9.4.1. t| Using Cross Equation Restrictions to Achieve Identification -- g| 9.4.2. t| Using Covariance Restrictions to Achieve Identification -- g| 9.4.3. t| Subtleties Concerning Identification and Efficiency in Linear Systems -- g| 9.5. t| Simultaneous Equations Models Nonlinear in Endogenous Variables -- g| 9.5.1. t| Identification -- g| 9.5.2. t| Estimation -- g| 9.5.3. t| Control Function Estimation for Triangular Systems -- g| 9.6. t| Different Instruments for Different Equations -- t| Problems -- g| 10. t| Basic Linear Unobserved Effects and Explanatory Variables -- g| 10.1. t| Motivation: Omitted Variables Problem -- g| 10.2. t| Assumptions about the Unobserved Effects and Explanatory Variables -- g| 10.2.1. t| Random or Fixed Effects? -- g| 10.2.2. t| Strict Exogeneity Assumptions on the Explanatory Variables -- g| 10.2.3. t| Some Examples of Unobserved Effects Panel Data Models -- g| 10.3. t| Estimating Unobserved Effects Models by Pooled Ordinary Least Squares -- g| 10.4. t| Random Effects Methods -- g| 10.4.1. t| Estimation and Inference under the Basic Random Effects Assumptions -- g| 10.4.2. t| Robust Variance Matrix Estimator -- g| 10.4.3. t| General Feasible Generalized Least Squares Analysis -- g| 10.4.4. t| Testing for the Presence of an Unobserved Effect -- g| 10.5. t| Fixed Effects Methods -- g| 10.5.1. t| Consistency of the Fixed Effects Estimator -- g| 10.5.2. t| Asymptotic Inference with Fixed Effects -- g| 10.5.3. t| Dummy Variable Regression -- g| 10.5.4. t| Serial Correlation and the Robust Variance Matrix Estimator -- g| 10.5.5. t| Fixed Effects Generalized Least Squares -- g| 10.5.6. t| Using Fixed Effects Estimation for Policy Analysis -- g| 10.6. t| First Differencing Methods -- g| 10.6.1. t| Inference -- g| 10.6.2. t| Robust Variance Matrix -- g| 10.6.3. t| Testing for Serial Correlation -- g| 10.6.4. t| Policy Analysis Using First Differencing -- g| 10.7. t| Comparison of Estimators -- g| 10.7.1. t| Fixed Effects versus First Differencing -- g| 10.7.2. t| Relationship between the Random Effects and Fixed Effects Estimators -- g| 10.7.3. t| Hausman Test Comparing Random Effects and Fixed Effects Estimators -- t| Problems -- g| 11. t| More Topics in Linear Unobserved Effects Models -- g| 11.1. t| Generalized Method of Moments Approaches to the Standard Linear Unobserved Effects Model -- g| 11.1.1. t| Equivalance between GMM 3SLS and Standard Estimators -- g| 11.1.2. t| Chamberlain's Approach to Unobserved Effects Models -- g| 11.2. t| Random and Fixed Effects Instrumental Variables Methods -- g| 11.3. t| Hausman and Taylor-Type Models -- g| 11.4. t| First Differencing Instrumental Variables Methods -- g| 11.5. t| Unobserved Effects Models with Measurement Error -- g| 11.6. t| Estimation under Sequential Exogeneity -- g| 11.6.1. t| General Framework --

a| Contents note continued: g| 11.6.2. t| Models with Lagged Dependent Variables -- g| 11.7. t| Models with Individual-Specific Slopes -- g| 11.7.1. t| Random Trend Model -- g| 11.7.2. t| General Models with Individual-Specific Slopes -- g| 11.7.3. t| Robustness of Standard Fixed Effects Methods -- g| 11.7.4. t| Testing for Correlated Random Slopes -- t| Problems -- g| III. t| GENERAL APPROACHES TO NONLINEAR ESTIMATION -- g| 12. t| M-Estimation, Nonlinear Regression, and Quantile Regression -- g| 12.1. t| Introduction -- g| 12.2. t| Identification, Uniform Convergence, and Consistency -- g| 12.3. t| Asymptotic Normality -- g| 12.4. t| Two-Step M-Estimators -- g| 12.4.1. t| Consistency -- g| 12.4.2. t| Asymptotic Normality -- g| 12.5. t| Estimating the Asymptotic Variance -- g| 12.5.1. t| Estimation without Nuisance Parameters -- g| 12.5.2. t| Adjustments for Two-Step Estimation -- g| 12.6. t| Hypothesis Testing -- g| 12.6.1. t| Wald Tests -- g| 12.6.2. t| Score (or Lagrange Multiplier) Tests -- g| 12.6.3. t| Tests Based on the Change in the Objective Function -- g| 12.6.4. t| Behavior of the Statistics under Alternatives -- g| 12.7. t| Optimization Methods -- g| 12.7.1. t| Newton-Raphson Method -- g| 12.7.2. t| Berndt, Hall, Hall, and Hausman Algorithm -- g| 12.7.3. t| Generalized Gauss-Newton Method -- g| 12.7.4. t| Concentrating Parameters out of the Objective Function -- g| 12.8. t| Simulation and Resampling Methods -- g| 12.8.1. t| Monte Carlo Simulation -- g| 12.8.2. t| Bootstrapping -- g| 12.9. t| Multivariate Nonlinear Regression Methods -- g| 12.9.1. t| Multivariate Nonlinear Least Squares -- g| 12.9.2. t| Weighted Multivariate Nonlinear Least Squares -- g| 12.10. t| Quantile Estimation -- g| 12.10.1. t| Quantiles, the Estimation Problem, and Consistency -- g| 12.10.2. t| Asymptotic Inference -- g| 12.10.3. t| Quantile Regression for Panel Data -- t| Problems -- g| 13. t| Maximum Likelihood Methods -- g| 13.1. t| Introduction -- g| 13.2. t| Preliminaries and Examples -- g| 13.3. t| General Framework for Conditional Maximum Likelihood Estimation -- g| 13.4. t| Consistency of Conditional Maximum Likelihood Estimation -- g| 13.5. t| Asymptotic Normality and Asymptotic Variance Estimation -- g| 13.5.1. t| Asymptotic Normality -- g| 13.5.2. t| Estimating the Asymptotic Variance -- g| 13.6. t| Hypothesis Testing -- g| 13.7. t| Specification Testing -- g| 13.8. t| Partial (or Pooled) Likelihood Methods for Panel Data -- g| 13.8.1. t| Setup for Panel Data -- g| 13.8.2. t| Asymptotic Inference -- g| 13.8.3. t| Inference with Dynamically Complete Models -- g| 13.9. t| Panel Data Models with Unobserved Effects -- g| 13.9.1. t| Models with Strictly Exogenous Explanatory Variables -- g| 13.9.2. t| Models with Lagged Dependent Variables -- g| 13.10. t| Two-Step Estimators Involving Maximum Likelihood -- g| 13.10.1. t| Second-Step Estimator Is Maximum Likelihood Estimator -- g| 13.10.2. t| Surprising Efficiency Result When the First-Step Estimator Is Conditional Maximum Likelihood Estimator -- g| 13.11. t| Quasi-Maximum Likelihood Estimation -- g| 13.11.1. t| General Misspecification -- g| 13.11.2. t| Model Selection Tests -- g| 13.11.3. t| Quasi-Maximum Likelihood Estimation in the Linear Exponential Family -- g| 13.11.4. t| Generalized Estimating Equations for Panel Data -- t| Problems -- t| Appendix 13A -- g| 14. t| Generalized Method of Moments and Minimum Distance Estimation -- g| 14.1. t| Asymptotic Properties of Generalized Method of Moments -- g| 14.2. t| Estimation under Orthogonality Conditions -- g| 14.3. t| Systems of Nonlinear Equations -- g| 14.4. t| Efficient Estimation -- g| 14.4.1. t| General Efficiency Framework -- g| 14.4.2. t| Efficiency of Maximum Likelihood Estimator -- g| 14.4.3. t| Efficienct Choice of Instruments under Conditional Moment Restrictions -- g| 14.5. t| Classical Minimum Distance Estimation -- g| 14.6. t| Panel Data Applications -- g| 14.6.1. t| Nonlinear Dynamic Models -- g| 14.6.2. t| Minimum Distance Approach to the Unobserved Effects Model -- g| 14.6.3. t| Models with Time-Varying Coefficients on the Unobserved Effects -- t| Problems -- t| Appendix 14A -- g| IV. t| NONLINEAR MODELS AND RELATED TOPICS -- g| 15. t| Binary Response Models -- g| 15.1. t| Introduction -- g| 15.2. t| Linear Probability Model for Binary Response -- g| 15.3. t| Index Models for Binary Response: Probit and Logit -- g| 15.4. t| Maximum Likelihood Estimation of Binary Response Index Models -- g| 15.5. t| Testing in Binary Response Index Models -- g| 15.5.1. t| Testing Multiple Exclusion Restrictions -- g| 15.5.2. t| Testing Nonlinear Hypotheses about β -- g| 15.5.3. t| Tests against More General Alternatives -- g| 15.6. t| Reporting the Results for Probit and Logit -- g| 15.7. t| Specification Issues in Binary Response Models -- g| 15.7.1. t| Neglected Heterogeneity -- g| 15.7.2. t| Continuous Endogenous Explanatory Variables -- g| 15.7.3. t| Binary Endogenous Explanatory Variable -- g| 15.7.4. t| Heteroskedasticity and Nonnormality in the Latent Variable Model -- g| 15.7.5. t| Estimation under Weaker Assumptions -- g| 15.8. t| Binary Response Models for Panel Data -- g| 15.8.1. t| Pooled Probit and Logit -- g| 15.8.2. t| Unobserved Effects Probit Models under Strict Exogeneity -- g| 15.8.3. t| Unobserved Effects Logit Models under Strict Exogeneity -- g| 15.8.4. t| Dynamic Unobserved Effects Models -- g| 15.8.5. t| Probit Models with Heterogeneity and Endogenous Explanatory Variables -- g| 15.8.6. t| Semiparametric Approaches -- t| Problems -- g| 16. t| Multinomial and Ordered Response Models -- g| 16.1. t| Introduction -- g| 16.2. t| Multinomial Response Models -- g| 16.2.1. t| Multinomial Logit -- g| 16.2.2. t| Probabilistic Choice Models -- g| 16.2.3. t| Endogenous Explanatory Variables -- g| 16.2.4. t| Panel Data Methods -- g| 16.3. t| Ordered Response Models -- g| 16.3.1. t| Ordered Logit and Ordered Probit -- g| 16.3.2. t| Specification Issues in Ordered Models -- g| 16.3.3. t| Endogenous Explanatory Variables -- g| 16.3.4. t| Panel Data Methods -- t| Problems -- g| 17. t| Corner Solution Responses -- g| 17.1. t| Motivation and Examples -- g| 17.2. t| Useful Expressions for Type I Tobit -- g| 17.3. t| Estimation and Inference with the Type I Tobit Model -- g| 17.4. t| Reporting the Results -- g| 17.5. t| Specification Issues in Tobit Models -- g| 17.5.1. t| Neglected Heterogeneity -- g| 17.5.2. t| Endogenous Explanatory Models -- g| 17.5.3. t| Heteroskedasticity and Nonnormality in the Latent Variable Model -- g| 17.5.4. t| Estimating Parameters with Weaker Assumptions -- g| 17.6. t| Two-Part Models and Type II Tobit for Corner Solutions -- g| 17.6.1. t| Truncated Normal Hurdle Model -- g| 17.6.2. t| Lognormal Hurdle Model and Exponential Conditional Mean -- g| 17.6.3. t| Exponential Type II Tobit Model -- g| 17.7. t| Two-Limit Tobit Model -- g| 17.8. t| Panel Data Methods -- g| 17.8.1. t| Pooled Methods -- g| 17.8.2. t| Unobserved Effects Models under Strict Exogeneity -- g| 17.8.3. t| Dynamic Unobserved Effects Tobit Models -- t| Problems -- g| 18. t| Count, Fractional, and Other Nonnegative Responses -- g| 18.1. t| Introduction -- g| 18.2. t| Poisson Regression -- g| 18.2.1. t| Assumptions Used for Poission Regression and Quantities of Interest -- g| 18.2.2. t| Consistency of the Poisson QMLE -- g| 18.2.3. t| Asymptotic Normality of the Poisson QMLE -- g| 18.2.4. t| Hypothesis Testing -- g| 18.2.5. t| Specification Testing -- g| 18.3. t| Other Count Data Regression Models -- g| 18.3.1. t| Negative Binomial Regression Models -- g| 18.3.2. t| Binomial Regression Models -- g| 18.4. t| Gamma (Exponential) Regression Model -- g| 18.5. t| Endogeneity with an Exponential Regression Function -- g| 18.6. t| Fractional Responses -- g| 18.6.1. t| Exogenous Explanatory Variables -- g| 18.6.2. t| Endogenous Explanatory Variables -- g| 18.7. t| Panel Data Methods -- g| 18.7.1. t| Pooled QMLE -- g| 18.7.2. t| Specifying Models of Conditional Expectations with Unobserved Effects -- g| 18.7.3. t| Random Effects Methods -- g| 18.7.4. t| Fixed Effects Poisson Estimation -- g| 18.7.5. t| Relaxing the Strict Exogeneity Assumption -- g| 18.7.6. t| Fractional Response Models for Panel Data -- t| Problems -- g| 19. t| Censored Data, Sample Selection, and Attrition -- g| 19.1. t| Introduction -- g| 19.2. t| Data Censoring -- g| 19.2.1. t| Binary Censoring -- g| 19.2.2. t| Interval Coding -- g| 19.2.3. t| Censoring from Above and Below -- g| 19.3. t| Overview of Sample Selection -- g| 19.4. t| When Can Sample Selection Be Ignored? -- g| 19.4.1. t| Linear Models: Estimation by OLS and 2SLS -- g| 19.4.2. t| Nonlinear Models -- g| 19.5. t| Selection on the Basis of the Response Variable: Truncated Regression -- g| 19.6. t| Incidental Truncation: A Probit Selection Equation -- g| 19.6.1. t| Exogenous Explanatory Variables -- g| 19.6.2. t| Endogenous Explanatory Variables -- g| 19.6.3. t| Binary Response Model with Sample Selection -- g| 19.6.4. t| Exponential Response Function -- g| 19.7. t| Incidental Truncation: A Tobit Selection Equation -- g| 19.7.1. t| Exogenous Explanatory Variables -- g| 19.7.2. t| Endogenous Explanatory Variables -- g| 19.7.3. t| Estimating Structural Tobit Equations with Sample Selection -- g| 19.8. t| Inverse Probability Weighting for Missing Data -- g| 19.9. t| Sample Selection and Attrition in Linear Panel Data Models -- g| 19.9.1. t| Fixed and Random Effects Estimation with Unbalanced Panels -- g| 19.9.2. t| Testing and Correcting for Sample Selection Bias -- g| 19.9.3. t| Attrition -- t| Problems -- g| 20. t| Stratified Sampling and Cluster Sampling -- g| 20.1. t| Introduction -- g| 20.2. t| Stratified Sampling -- g| 20.2.1. t| Standard Stratified Sampling and Variable Probability Sampling -- g| 20.2.2. t| Weighted Estimators to Account for Stratification -- g| 20.2.3. t| Stratification Based on Exogenous Variables -- g| 20.3. t| Cluster Sampling -- g| 20.3.1. t| Inference with a Large Number of Clusters and Small Cluster Sizes -- g| 20.3.2. t| Cluster Samples with Unit-Specific Panel Data -- g| 20.3.3. t| Should We Apply Cluster-Robust Inference with Large Group Sizes? -- g| 20.3.4. t| Inference When the Number of Clusters Is Small -- g| 20.4. t| Complex Survey Sampling -- t| Problems -- g| 21. t| Estimating Average Treatment Effects -- g| 21.1. t| Introduction -- g| 21.2. t| Counterfactual Setting and the Self-Selection Problem -- g| 21.3. t| Methods Assuming Ignorability (or Unconfoundedness) of Treatment -- g| 21.3.1. t| Identification --

a| Contents note continued: g| 21.3.2. t| Regression Adjustment -- g| 21.3.3. t| Propensity Score Methods -- g| 21.3.4. t| Combining Regression Adjustment and Propensity Score Weighting -- g| 21.3.5. t| Matching Methods -- g| 21.4. t| Instrumental Variables Methods -- g| 21.4.1. t| Estimating the Average Treatment Effect Using IV -- g| 21.4.2. t| Correction and Control Function Approaches -- g| 21.4.3. t| Estimating the Local Average Treatment Effect by IV -- g| 21.5. t| Regression Discontinuity Designs -- g| 21.5.1. t| Sharp Regression Discontinuity Design -- g| 21.5.2. t| Fuzzy Regression Discontinuity Design -- g| 21.5.3. t| Unconfoundedness versus the Fuzzy Regression Discontinuity -- g| 21.6. t| Further Issues -- g| 21.6.1. t| Special Considerations for Responses with Discreteness or Limited Range -- g| 21.6.2. t| Multivalued Treatments -- g| 21.6.3. t| Multiple Treatments -- g| 21.6.4. t| Panel Data -- t| Problems -- g| 22. t| Duration Analysis -- g| 22.1. t| Introduction -- g| 22.2. t| Hazard Functions -- g| 22.2.1. t| Hazard Functions without Covariates -- g| 22.2.2. t| Hazard Functions Conditional on Time-Invariant Covariates -- g| 22.2.3. t| Hazard Functions Conditional on Time-Varying Covariates -- g| 22.3. t| Analysis of Single-Spell Data with Time-Invariant Covariates -- g| 22.3.1. t| Flow Sampling -- g| 22.3.2. t| Maximum Likelihood Estimation with Censored Flow Data -- g| 22.3.3. t| Stock Sampling -- g| 22.3.4. t| Unobserved Heterogeneity -- g| 22.4. t| Analysis of Grouped Duration Data -- g| 22.4.1. t| Time-Invariant Covariates -- g| 22.4.2. t| Time-Varying Covariates -- g| 22.4.3. t| Unobserved Heterogeneity -- g| 22.5. t| Further Issues -- g| 22.5.1. t| Cox's Partial Likelihood Method for the Proportional Hazard Model -- g| 22.5.2. t| Multiple-Spell Data -- g| 22.5.3. t| Competing Risks Models -- t| Problems.

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