Template-Type: ReDIF-Paper 1.0

Author-Name: Ivan Fernandez-Val

Author-X-Name-First: Ivan 
Author-X-Name-Last: Fernandez-Val

Author-Email: ivanf@bu.edu
Author-Workplace-Name: Department of Economics, Boston University

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Author-X-Name-First: 
Author-X-Name-Last: 
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Title: Estimation of Structural Parameters and Marginal Effects in
Binary Choice Panel Data Models with Fixed Effects


Abstract: Fixed e容cts estimates of structural parameters in nonlinear panel models can be severely biased
due to the incidental parameters problem. In this paper I show that the most important com-
ponent of this incidental parameters bias for probit 矩ed e容cts estimators of index coe帷ients
is proportional to the true parameter value, using a large-T expansion of the bias. This result
allows me to derive a lower bound for this bias, and to show that 矩ed e容cts estimates of ratios
of coe帷ients and average marginal e容cts have zero bias in the absence of heterogeneity and
have negligible bias relative to their true values for a wide range of distributions of regressors and
individual e容cts. Numerical examples suggest that this small bias property also holds for logit
and linear probability models, and for exogenous variables in dynamic binary choice models.
An empirical analysis of female labor force participation using data from the PSID shows that
whereas the signi畚ant biases in 矩ed e容cts estimates of model parameters do not contami-
nate the estimates of marginal e容cts in static models, estimates of both index coe帷ients and
marginal e容cts can be severely biased in dynamic models. Improved bias corrected estimators
for index coe帷ients and marginal e容cts are also proposed for both static and dynamic models.


Length:  55 pages
Creation-Date: 2005-10
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File-URL: http://www.bu.edu/econ/workingpapers/papers/Ivan%20Fernandez-Val/draft_sep22_2005.pdf
File-Format: Application/pdf
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Number: WP2005-38
Classification-JEL: C23; C25; J22.

Keywords: 
Handle: RePEc:bos:wpaper:WP2005-38