Estimating Dynamic Panel Data Discrete Choice Models with Fixed Effects
Jesus M. Carro
Centre for Monetary and Financial Studies (CEMFI)
CEMFI Working Paper No. 0304
In this paper, I consider the estimation of dynamic binary choice panel data models with fixed effects. I use a Modified Maximum Likelihood Estimator (MMLE) that reduces the order of the bias in the Maximum Likelihood Estimator from O(T-1) to O(T-2), without increasing the asymptotic variance. I evaluate its performance in finite samples where T is not large, using Monte Carlo simulations. In Probit and Logit models containing lags of the endogenous variable and exogenous variables, the estimator is found to have a small bias in a panel with eight periods. A distinctive advantage of the MMLE is its general applicability. Identification issues about policy parameters of interest that arise in this kind of models are also addressed. In contrast with linear models, parameters of interest typically depend on the distribution of the individual effects. I discuss the relevance of mean effects across individuals and show an instance in which the entire distribution is needed. Compared with simple MLE, simulation results show that MMLE improves significantly the estimation of the distribution of the effect of interest.
Number of Pages in PDF File: 43
Keywords: Panel data, dynamic discrete choice, fixed effects, modified MMLE
JEL Classification: C23, J22working papers series
Date posted: March 24, 2003
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