Abstract
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.
Original language | English |
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Pages (from-to) | 925-958 |
Number of pages | 34 |
Journal | International Economic Review |
Volume | 51 |
Issue number | 4 |
Early online date | 18 Nov 2010 |
DOIs | |
Publication status | Published - Nov 2010 |
Keywords
- Initial Conditions
- Missing Data
- Classification Error
- Simulated Maximum Likelihood
- Female Labor Supply