Microeconometrics : Methods and Applications

by A. Colin Cameron , Pravin K. Trivedi

• ISBN13: 9780521848053
• ISBN10: 0521848059
• Format: Hardcover
• Copyright: 2005-05-09
• Publisher: Cambridge University Press

Summary

This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.

Author Biography

A. Colin Cameron is Professor of Economics at the University of California, Davis Pravin K. Trivedi is John H. Rudy Professor of Economics at Indiana University at Bloomington

Table of Contents

List of Figures	xv
List of Tables	xvii
Preface	xxi
I Preliminaries
Overview	3	(15)
Introduction	3	(2)
Distinctive Aspects of Microeconometrics	5	(5)
Book Outline	10	(4)
How to Use This Book	14	(1)
Software	15	(1)
Notation and Conventions	16	(2)
Causal and Noncausal Models	18	(21)
Introduction	18	(2)
Structural Models	20	(2)
Exogeneity	22	(1)
Linear Simultaneous Equations Model	23	(6)
Identification Concepts	29	(2)
Single-Equation Models	31	(1)
Potential Outcome Model	31	(4)
Causal Modeling and Estimation Strategies	35	(3)
Bibliographic Notes	38	(1)
Microeconomic Data Structures	39	(26)
Introduction	39	(1)
Observational Data	40	(8)
Data from Social Experiments	48	(6)
Data from Natural Experiments	54	(4)
Practical Considerations	58	(3)
Bibliographic Notes	61	(4)
II Core Methods
Linear Models	65	(51)
Introduction	65	(1)
Regressions and Loss Functions	66	(3)
Example: Returns to Schooling	69	(1)
Ordinary Least Squares	70	(11)
Weighted Least Squares	81	(4)
Median and Quantile Regression	85	(5)
Model Misspecification	90	(5)
Instrumental Variables	95	(8)
Instrumental Variables in Practice	103	(9)
Practical Considerations	112	(1)
Bibliographic Notes	112	(4)
Maximum Likelihood and Nonlinear Least-Squares Estimation	116	(50)
Introduction	116	(1)
Overview of Nonlinear Estimators	117	(7)
Extremum Estimators	124	(9)
Estimating Equations	133	(2)
Statistical Inference	135	(4)
Maximum Likelihood	139	(7)
Quasi-Maximum Likelihood	146	(4)
Nonlinear Least Squares	150	(9)
Example: ML and NLS Estimation	159	(4)
Practical Considerations	163	(1)
Bibliographic Notes	163	(3)
Generalized Method of Moments and Systems Estimation	166	(57)
Introduction	166	(1)
Examples	167	(5)
Generalized Method of Moments	172	(11)
Linear Instrumental Variables	183	(9)
Nonlinear Instrumental Variables	192	(8)
Sequential Two-Step m-Estimation	200	(2)
Minimum Distance Estimation	202	(1)
Empirical Likelihood	203	(3)
Linear Systems of Equations	206	(8)
Nonlinear Sets of Equations	214	(5)
Practical Considerations	219	(1)
Bibliographic Notes	220	(3)
Hypothesis Tests	223	(36)
Introduction	223	(1)
Wald Test	224	(9)
Likelihood-Based Tests	233	(8)
Example: Likelihood-Based Hypothesis Tests	241	(2)
Tests in Non-ML Settings	243	(3)
Power and Size of Tests	246	(4)
Monte Carlo Studies	250	(4)
Bootstrap Example	254	(2)
Practical Considerations	256	(1)
Bibliographic Notes	257	(2)
Specification Tests and Model Selection	259	(35)
Introduction	259	(1)
m-Tests	260	(11)
Hausman Test	271	(3)
Tests for Some Common Misspecifications	274	(4)
Discriminating between Nonnested Models	278	(7)
Consequences of Testing	285	(2)
Model Diagnostics	287	(4)
Practical Considerations	291	(1)
Bibliographic Notes	292	(2)
Semiparametric Methods	294	(42)
Introduction	294	(1)
Nonparametric Example: Hourly Wage	295	(3)
Kernel Density Estimation	298	(9)
Nonparametric Local Regression	307	(4)
Kernel Regression	311	(8)
Alternative Nonparametric Regression Estimators	319	(3)
Semiparametric Regression	322	(8)
Derivations of Mean and Variance of Kernel Estimators	330	(3)
Practical Considerations	333	(1)
Bibliographic Notes	333	(3)
Numerical Optimization	336	(21)
Introduction	336	(1)
General Considerations	336	(5)
Specific Methods	341	(7)
Practical Considerations	348	(4)
Bibliographic Notes	352	(5)
III Simulation-Based Methods
Bootstrap Methods	357	(27)
Introduction	357	(1)
Bootstrap Summary	358	(8)
Bootstrap Example	366	(2)
Bootstrap Theory	368	(5)
Bootstrap Extensions	373	(3)
Bootstrap Applications	376	(6)
Practical Considerations	382	(1)
Bibliographic Notes	382	(2)
Simulation-Based Methods	384	(35)
Introduction	384	(1)
Examples	385	(2)
Basics of Computing Integrals	387	(6)
Maximum Simulated Likelihood Estimation	393	(5)
Moment-Based Simulation Estimation	398	(6)
Indirect Inference	404	(2)
Simulators	406	(4)
Methods of Drawing Random Variates	410	(6)
Bibliographic Notes	416	(3)
Bayesian Methods	419	(44)
Introduction	419	(1)
Bayesian Approach	420	(15)
Bayesian Analysis of Linear Regression	435	(8)
Monte Carlo Integration	443	(2)
Markov Chain Monte Carlo Simulation	445	(7)
MCMC Example: Gibbs Sampler for SUR	452	(2)
Data Augmentation	454	(2)
Bayesian Model Selection	456	(2)
Practical Considerations	458	(1)
Bibliographic Notes	458	(5)
IV Models for Cross-Section Data
Binary Outcome Models	463	(27)
Introduction	463	(1)
Binary Outcome Example: Fishing Mode Choice	464	(1)
Logit and Probit Models	465	(10)
Latent Variable Models	475	(3)
Choice-Based Samples	478	(2)
Grouped and Aggregate Data	480	(2)
Semiparametric Estimation	482	(4)
Derivation of Logit from Type I Extreme Value	486	(1)
Practical Considerations	487	(1)
Bibliographic Notes	487	(3)
Multinomial Models	490	(39)
Introduction	490	(1)
Example: Choice of Fishing Mode	491	(4)
General Results	495	(5)
Multinomial Logit	500	(4)
Additive Random Utility Models	504	(3)
Nested Logit	507	(5)
Random Parameters Logit	512	(4)
Multinomial Probit	516	(3)
Ordered, Sequential, and Ranked Outcomes	519	(2)
Multivariate Discrete Outcomes	521	(2)
Semiparametric Estimation	523	(1)
Derivations for MNL, CL, and NL Models	524	(3)
Practical Considerations	527	(1)
Bibliographic Notes	528	(1)
Tobit and Selection Models	529	(44)
Introduction	529	(1)
Censored and Truncated-Models	530	(6)
Tobit Model	536	(8)
Two-Part Model	544	(2)
Sample Selection Models	546	(7)
Selection Example: Health Expenditures	553	(2)
Roy Model	555	(3)
Structural Models	558	(4)
Semiparametric Estimation	562	(4)
Derivations for the Tobit Model	566	(2)
Practical Considerations	568	(1)
Bibliographic Notes	569	(4)
Transition Data: Survival Analysis	573	(38)
Introduction	573	(1)
Example: Duration of Strikes	574	(2)
Basic Concepts	576	(3)
Censoring	579	(1)
Nonparametric Models	580	(4)
Parametric Regression Models	584	(7)
Some Important Duration Models	591	(1)
Cox PH Model	592	(5)
Time-Varying Regressors	597	(3)
Discrete-Time Proportional Hazards	600	(3)
Duration Example: Unemployment Duration	603	(5)
Practical Considerations	608	(1)
Bibliographic Notes	608	(3)
Mixture Models and Unobserved Heterogeneity	611	(29)
Introduction	611	(1)
Unobserved Heterogeneity and Dispersion	612	(6)
Identification in Mixture Models	618	(2)
Specification of the Heterogeneity Distribution	620	(1)
Discrete Heterogeneity and Latent Class Analysis	621	(4)
Stock and Flow Sampling	625	(3)
Specification Testing	628	(4)
Unobserved Heterogeneity Example: Unemployment Duration	632	(5)
Practical Considerations	637	(1)
Bibliographic Notes	637	(3)
Models of Multiple Hazards	640	(25)
Introduction	640	(2)
Competing Risks	642	(6)
Joint Duration Distributions	648	(7)
Multiple Spells	655	(3)
Competing Risks Example: Unemployment Duration	658	(4)
Practical Considerations	662	(1)
Bibliographic Notes	663	(2)
Models of Count Data	665	(32)
Introduction	665	(1)
Basic Count Data Regression	666	(5)
Count Example: Contacts with Medical Doctor	671	(3)
Parametric Count Regression Models	674	(8)
Partially Parametric Models	682	(3)
Multivariate Counts and Endogenous Regressors	685	(5)
Count Example: Further Analysis	690	(1)
Practical Considerations	690	(1)
Bibliographic Notes	691	(6)
V Models for Panel Data
Linear Panel Models: Basics	697	(46)
Introduction	697	(1)
Overview of Models and Estimators	698	(10)
Linear Panel Example: Hours and Wages	708	(7)
Fixed Effects versus Random Effects Models	715	(5)
Pooled Models	720	(6)
Fixed Effects Model	726	(8)
Random Effects Model	734	(3)
Modeling Issues	737	(3)
Practical Considerations	740	(1)
Bibliographic Notes	740	(3)
Linear Panel Models: Extensions	743	(36)
Introduction	743	(1)
GMM Estimation of Linear Panel Models	744	(10)
Panel GMM Example: Hours and Wages	754	(2)
Random and Fixed Effects Panel GMM	756	(7)
Dynamic Models	763	(5)
Difference-in-Differences Estimator	768	(2)
Repeated Cross Sections and Pseudo Panels	770	(4)
Mixed Linear Models	774	(2)
Practical Considerations	776	(1)
Bibliographic Notes	777	(2)
Nonlinear Panel Models	779	(34)
Introduction	779	(1)
General Results	779
Nonlinear Panel Example: Patents and R&D	762	(33)
Binary Outcome Data	795	(5)
Tobit and Selection Models	800	(1)
Transition Data	801	(1)
Count Data	802	(6)
Semiparametric Estimation	808	(1)
Practical Considerations	808	(1)
Bibliographic Notes	809	(4)
VI Further Topics
Stratified and Clustered Samples	813	(47)
Introduction	813	(1)
Survey Sampling	814	(3)
Weighting	817	(5)
Endogenous Stratification	822	(7)
Clustering	829	(16)
Hierarchical Linear Models	845	(3)
Clustering Example: Vietnam Health Care Use	848	(5)
Complex Surveys	853	(4)
Practical Considerations	857	(1)
Bibliographic Notes	857	(3)
Treatment Evaluation	860	(39)
Introduction	860	(2)
Setup and Assumptions	862	(3)
Treatment Effects and Selection Bias	865	(6)
Matching and Propensity Score Estimators	871	(7)
Differences-in-Differences Estimators	878	(1)
Regression Discontinuity Design	879	(4)
Instrumental Variable Methods	883	(6)
Example: The Effect of Training on Earnings	889	(7)
Bibliographic Notes	896	(3)
Measurement Error Models	899	(24)
Introduction	899	(1)
Measurement Error in Linear Regression	900	(5)
Identification Strategies	905	(6)
Measurement Errors in Nonlinear Models	911	(8)
Attenuation Bias Simulation Examples	919	(1)
Bibliographic Notes	920	(3)
Missing Data and Imputation	923	(20)
Introduction	923	(2)
Missing Data Assumptions	925	(3)
Handling Missing Data without Models	928	(1)
Observed-Data Likelihood	929	(1)
Regression-Based Imputation	930	(2)
Data Augmentation and MCMC	932	(2)
Multiple Imputation	934	(1)
Missing Data MCMC Imputation Example	935	(4)
Practical Considerations	939	(1)
Bibliographic Notes	940	(3)
A Asymptotic Theory	943	(14)
Introduction	943	(1)
Convergence in Probability	944	(3)
Laws of Large Numbers	947	(1)
Convergence in Distribution	948	(1)
Central Limit Theorems	949	(2)
Multivariate Normal Limit Distributions	951	(3)
Stochastic Order of Magnitude	954	(1)
Other Results	955	(1)
Bibliographic Notes	956	(1)
B Making Pseudo-Random Draws	957	(4)
References	961	(38)
Index	999