A COMPUTATIONALLY PRACTICAL SIMULATION ESTIMATION ALGORITHM FOR DYNAMIC PANEL DATA MODELS WITH UNOBSERVED ENDOGENOUS STATE VARIABLES

This article develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample...

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Bibliographic Details
Published in:International economic review (Philadelphia) 2010-11, Vol.51 (4), p.925-958
Main Authors: Keane, Michael P., Sauer, Robert M.
Format: Article
Language:English
Subjects:
Algorithms
Computational methods
Data models
Decision making
Dynamic models
Economic models
Education
Error rates
Estimating techniques
Estimation bias
Female labour
Females
Labor economics
Labor supply
Labour supply
Markov models
Modeling
Observed choices
Parametric models
Preliminary estimates
Race
Sampling bias
Simulation
Simulations
Standard deviation
Statistical models
Studies
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Description
Summary:This article develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample properties. We apply the estimator to a model of female labor supply and show that the rarely used Polya model fits the data substantially better than the popular Markov model. The Polya model also produces far less state dependence and many fewer race effects and much stronger effects of education, young children, and husband's income on female labor supply decisions.
ISSN:0020-6598
1468-2354
DOI:10.1111/j.1468-2354.2010.00606.x