A computationally practical simulation estimation algorithm for dynamic panel data models with unobserved endogenous state variables
- Publication Type:
- Journal Article
- Citation:
- International Economic Review, 2010, 51 (4), pp. 925 - 958
- Issue Date:
- 2010-11-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | 2009000306OK.pdf | 298.53 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
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. © (2010) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
Please use this identifier to cite or link to this item: