Econometric Analysis of Cross Section and Panel Data

ISBN-10: 0262232588

ISBN-13: 9780262232586

Edition: 2nd 2007

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The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Datawas the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
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Book details

List price: $115.00
Edition: 2nd
Copyright year: 2007
Publisher: MIT Press
Publication date: 10/1/2010
Binding: Hardcover
Pages: 1096
Size: 8.50" wide x 9.50" long x 1.75" tall
Weight: 4.466
Language: English

Jeffrey M. Wooldridge is University Distinguished Professor of Economics at Michigan State University and a Fellow of the Econometric Society.

Introduction and Background
Causal Relationships and Ceteris Paribus Analysis
Stochastic Setting and Asymptotic Analysis
Some Examples
Why Not Fixed Explanatory Variables?
Conditional Expectations and Related Concepts in Econometrics
Role of Conditional Expectations in Econometrics
Features of Conditional Expectations
Linear Projections
Appendix 2A
Basic Asymptotic Theory
Convergence of Deterministic Sequences
Convergence in Probability and Boundedness in Probability
Convergence in Distribution
Limit Theorems for Random Samples
Limiting Behavior of Estimators and Test Statistics
Linear Models
Single-Equation Linear Model and Ordinary Least Squares Estimation
Overview of the Single-Equation Linear Model
Asymptotic Properties of Ordinary Least Squares
Ordinary Least Squares Solutions to the Omitted Variables Problem
Properties of Ordinary Least Squares under Measurement Error
Instrumental Variables Estimation of Single-Equation Linear Models
Instrumental Variables and Two-Stage Least Squares
General Treatment of Two-Stage Least Squares
IV Solutions to the Omitted Variables and Measurement Error Problems
Additional Single-Equation Topics
Estimation with Generated Regressors and Instruments
Control Function Approach to Endogeneity
Some Specification Tests
Correlated Random Coe‰cient Models
Pooled Cross Sections and Di�erence-in-Di�erences Estimation
Appendix 6A
Estimating Systems of Equations by Ordinary Least Squares and Generalized Least Squares
Some Examples
System Ordinary Least Squares Estimation of a Multivariate Linear System
Consistency and Asymptotic Normality of Generalized Least Squares
Feasible Generalized Least Squares
Testing the Use of Feasible Generalized Least Squares
Seemingly Unrelated Regressions, Revisited
Linear Panel Data Model, Revisited
System Estimation by Instrumental Variables
Introduction and Examples
General Linear System of Equations
Generalized Method of Moments Estimation
Generalized Instrumental Variables Estimator
Testing Using Generalized Method of Moments
More E‰cient Estimation and Optimal Instruments
Summary Comments on Choosing an Estimator
Simultaneous Equations Models
Scope of Simultaneous Equations Models
Identification in a Linear System
Estimation after Identification
Additional Topics in Linear Simultaneous Equations Methods
Simultaneous Equations Models Nonlinear in Endogenous Variables
Di�erent Instruments for Di�erent Equations
Basic Linear Unobserved E�ects Panel Data Models
Motivation: Omitted Variables Problem
Assumptions about the Unobserved E�ects and Explanatory Variables
Estimating Unobserved E�ects Models by Pooled Ordinary Least Squares
Random E�ects Methods
Fixed E�ects Methods
First Differencing Methods
Comparison of Estimators
More Topics in Linear Unobserved Effects Models
Generalized Method of Moments Approaches to the Standard Linear Unobserved E�ects Model
Random and Fixed E�ects Instrumental Variables Methods
Hausman and Taylor-Type Models
First Di�erencing Instrumental Variables Methods
Unobserved Effects Models with Measurement Error
Estimation under Sequential Exogeneity
Models with Individual-Specific Slopes
General Approaches to Nonlinear Estimation
M-Estimation, Nonlinear Regression, and Quantile Regression
Identification, Uniform Convergence, and Consistency
Asymptotic Normality
Two-Step M-Estimators
Estimating the Asymptotic Variance
Hypothesis Testing
Optimization Methods
Simulation and Resampling Methods
Multivariate Nonlinear Regression Methods
Quantile Estimation
Maximum Likelihood Methods
Preliminaries and Examples
General Framework for Conditional Maximum Likelihood Estimation
Consistency of Conditional Maximum Likelihood Estimation
Asymptotic Normality and Asymptotic Variance Estimation
Hypothesis Testing
Specification Testing
Partial (or Pooled) Likelihood Methods for Panel Data
Panel Data Models with Unobserved E�ects
Two-Step Estimators Involving Maximum Likelihood
Quasi-Maximum Likelihood Estimation
Appendix 13A
Generalized Method of Moments and Minimum Distance Estimation
Asymptotic Properties of Generalized Method of Moments
Estimation under Orthogonality Conditions
Systems of Nonlinear Equations
Efficient Estimation
Classical Minimum Distance Estimation
Panel Data Applications
Appendix 14A
Nonlinear Models and Related Topics
Binary Response Models
Linear Probability Model for Binary Response
Index Models for Binary Response: Probit and Logit
Maximum Likelihood Estimation of Binary Response Index Models
Testing in Binary Response Index Models
Reporting the Results for Probit and Logit
Specification Issues in Binary Response Models
Binary Response Models for Panel Data
Multinomial and Ordered Response Models
Multinomial Response Models
Ordered Response Models
Corner Solution Responses
Motivation and Examples
Useful Expressions for Type I Tobit
Estimation and Inference with the Type I Tobit Model
Reporting the Results
Specification Issues in Tobit Models
Two-Part Models and Type II Tobit for Corner Solutions
Two-Limit Tobit Model
Panel Data Methods
Count, Fractional, and Other Nonnegative Responses
Poisson Regression
Other Count Data Regression Models
Gamma (Exponential) Regression Model
Endogeneity with an Exponential Regression Function
Fractional Responses
Panel Data Methods
Censored Data, Sample Selection, and Attrition
Data Censoring
Overview of Sample Selection
When Can Sample Selection Be Ignored?
Selection on the Basis of the Response Variable: Truncated Regression
Incidental Truncation: A Probit Selection Equation
Incidental Truncation: A Tobit Selection Equation
Inverse Probability Weighting for Missing Data
Sample Selection and Attrition in Linear Panel Data Models
Stratified Sampling and Cluster Sampling
Stratified Sampling
Cluster Sampling
Complex Survey Sampling
Estimating Average Treatment Effects
A Counterfactual Setting and the Self-Selection Problem
Methods Assuming Ignorability (or Unconfoundedness) of Treatment
Instrumental Variables Methods
Regression Discontinuity Designs
Further Issues
Duration Analysis
Hazard Functions
Analysis of Single-Spell Data with Time-Invariant Covariates
Analysis of Grouped Duration Data
Further Issues
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