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Microeconometrics Methods and Applications

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ISBN-10: 0521848059

ISBN-13: 9780521848053

Edition: 2005

Authors: A. Colin Cameron, Pravin K. Trivedi

List price: $105.00
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Description:

The authors provide a comprehensive text on microeconometrics, the analysis of individual-level data on the economic behaviour of individuals or firms using regression methods for cross section and panel data.
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Book details

List price: $105.00
Copyright year: 2005
Publisher: Cambridge University Press
Publication date: 5/9/2005
Binding: Hardcover
Pages: 1056
Size: 7.28" wide x 10.35" long x 1.73" tall
Weight: 4.202
Language: English

List of Figures
List of Tables
Preface
Preliminaries
Overview
Introduction
Distinctive Aspects of Microeconometrics
Book Outline
How to Use This Book
Software
Notation and Conventions
Causal and Noncausal Models
Introduction
Structural Models
Exogeneity
Linear Simultaneous Equations Model
Identification Concepts
Single-Equation Models
Potential Outcome Model
Causal Modeling and Estimation Strategies
Bibliographic Notes
Microeconomic Data Structures
Introduction
Observational Data
Data from Social Experiments
Data from Natural Experiments
Practical Considerations
Bibliographic Notes
Core Methods
Linear Models
Introduction
Regressions and Loss Functions
Example: Returns to Schooling
Ordinary Least Squares
Weighted Least Squares
Median and Quantile Regression
Model Misspecification
Instrumental Variables
Instrumental Variables in Practice
Practical Considerations
Bibliographic Notes
Maximum Likelihood and Nonlinear Least-Squares Estimation
Introduction
Overview of Nonlinear Estimators
Extremum Estimators
Estimating Equations
Statistical Inference
Maximum Likelihood
Quasi-Maximum Likelihood
Nonlinear Least Squares
Example: ML and NLS Estimation
Practical Considerations
Bibliographic Notes
Generalized Method of Moments and Systems Estimation
Introduction
Examples
Generalized Method of Moments
Linear Instrumental Variables
Nonlinear Instrumental Variables
Sequential Two-Step m-Estimation
Minimum Distance Estimation
Empirical Likelihood
Linear Systems of Equations
Nonlinear Sets of Equations
Practical Considerations
Bibliographic Notes
Hypothesis Tests
Introduction
Wald Test
Likelihood-Based Tests
Example: Likelihood-Based Hypothesis Tests
Tests in Non-ML Settings
Power and Size of Tests
Monte Carlo Studies
Bootstrap Example
Practical Considerations
Bibliographic Notes
Specification Tests and Model Selection
Introduction
m-Tests
Hausman Test
Tests for Some Common Misspecifications
Discriminating between Nonnested Models
Consequences of Testing
Model Diagnostics
Practical Considerations
Bibliographic Notes
Semiparametric Methods
Introduction
Nonparametric Example: Hourly Wage
Kernel Density Estimation
Nonparametric Local Regression
Kernel Regression
Alternative Nonparametric Regression Estimators
Semiparametric Regression
Derivations of Mean and Variance of Kernel Estimators
Practical Considerations
Bibliographic Notes
Numerical Optimization
Introduction
General Considerations
Specific Methods
Practical Considerations
Bibliographic Notes
Simulation-Based Methods
Bootstrap Methods
Introduction
Bootstrap Summary
Bootstrap Example
Bootstrap Theory
Bootstrap Extensions
Bootstrap Applications
Practical Considerations
Bibliographic Notes
Simulation-Based Methods
Introduction
Examples
Basics of Computing Integrals
Maximum Simulated Likelihood Estimation
Moment-Based Simulation Estimation
Indirect Inference
Simulators
Methods of Drawing Random Variates
Bibliographic Notes
Bayesian Methods
Introduction
Bayesian Approach
Bayesian Analysis of Linear Regression
Monte Carlo Integration
Markov Chain Monte Carlo Simulation
MCMC Example: Gibbs Sampler for SUR
Data Augmentation
Bayesian Model Selection
Practical Considerations
Bibliographic Notes
Models for Cross-Section Data
Binary Outcome Models
Introduction
Binary Outcome Example: Fishing Mode Choice
Logit and Probit Models
Latent Variable Models
Choice-Based Samples
Grouped and Aggregate Data
Semiparametric Estimation
Derivation of Logit from Type I Extreme Value
Practical Considerations
Bibliographic Notes
Multinomial Models
Introduction
Example: Choice of Fishing Mode
General Results
Multinomial Logit
Additive Random Utility Models
Nested Logit
Random Parameters Logit
Multinomial Probit
Ordered, Sequential, and Ranked Outcomes
Multivariate Discrete Outcomes
Semiparametric Estimation
Derivations for MNL, CL, and NL Models
Practical Considerations
Bibliographic Notes
Tobit and Selection Models
Introduction
Censored and Truncated Models
Tobit Model
Two-Part Model
Sample Selection Models
Selection Example: Health Expenditures
Roy Model
Structural Models
Semiparametric Estimation
Derivations for the Tobit Model
Practical Considerations
Bibliographic Notes
Transition Data: Survival Analysis
Introduction
Example: Duration of Strikes
Basic Concepts
Censoring
Nonparametric Models
Parametric Regression Models
Some Important Duration Models
Cox PH Model
Time-Varying Regressors
Discrete-Time Proportional Hazards
Duration Example: Unemployment Duration
Practical Considerations
Bibliographic Notes
Mixture Models and Unobserved Heterogeneity
Introduction
Unobserved Heterogeneity and Dispersion
Identification in Mixture Models
Specification of the Heterogeneity Distribution
Discrete Heterogeneity and Latent Class Analysis
Stock and Flow Sampling
Specification Testing
Unobserved Heterogeneity Example: Unemployment Duration
Practical Considerations
Bibliographic Notes
Models of Multiple Hazards
Introduction
Competing Risks
Joint Duration Distributions
Multiple Spells
Competing Risks Example: Unemployment Duration
Practical Considerations
Bibliographic Notes
Models of Count Data
Introduction
Basic Count Data Regression
Count Example: Contacts with Medical Doctor
Parametric Count Regression Models
Partially Parametric Models
Multivariate Counts and Endogenous Regressors
Count Example: Further Analysis
Practical Considerations
Bibliographic Notes
Models for Panel Data
Linear Panel Models: Basics
Introduction
Overview of Models and Estimators
Linear Panel Example: Hours and Wages
Fixed Effects versus Random Effects Models
Pooled Models
Fixed Effects Model
Random Effects Model
Modeling Issues
Practical Considerations
Bibliographic Notes
Linear Panel Models: Extensions
Introduction
GMM Estimation of Linear Panel Models
Panel GMM Example: Hours and Wages
Random and Fixed Effects Panel GMM
Dynamic Models
Difference-in-Differences Estimator
Repeated Cross Sections and Pseudo Panels
Mixed Linear Models
Practical Considerations
Bibliographic Notes
Nonlinear Panel Models
Introduction
General Results
Nonlinear Panel Example: Patents and R&D
Binary Outcome Data
Tobit and Selection Models
Transition Data
Count Data
Semiparametric Estimation
Practical Considerations
Bibliographic Notes
Further Topics
Stratified and Clustered Samples
Introduction
Survey Sampling
Weighting
Endogenous Stratification
Clustering
Hierarchical Linear Models
Clustering Example: Vietnam Health Care Use
Complex Surveys
Practical Considerations
Bibliographic Notes
Treatment Evaluation
Introduction
Setup and Assumptions
Treatment Effects and Selection Bias
Matching and Propensity Score Estimators
Differences-in-Differences Estimators
Regression Discontinuity Design
Instrumental Variable Methods
Example: The Effect of Training on Earnings
Bibliographic Notes
Measurement Error Models
Introduction
Measurement Error in Linear Regression
Identification Strategies
Measurement Errors in Nonlinear Models
Attenuation Bias Simulation Examples
Bibliographic Notes
Missing Data and Imputation
Introduction
Missing Data Assumptions
Handling Missing Data without Models
Observed-Data Likelihood
Regression-Based Imputation
Data Augmentation and MCMC
Multiple Imputation
Missing Data MCMC Imputation Example
Practical Considerations
Bibliographic Notes
Asymptotic Theory
Introduction
Convergence in Probability
Laws of Large Numbers
Convergence in Distribution
Central Limit Theorems
Multivariate Normal Limit Distributions
Stochastic Order of Magnitude
Other Results
Bibliographic Notes
Making Pseudo-Random Draws
References
Index
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