| 主讲人 |
Andrew Adrian Pua |
简介 |
<p>Researchers have applied linear panel data methods to estimate binary choice models while allowing for individual-specific unobserved heterogeneity and dynamics either to provide empirical findings or to demonstrate the robustness of their empirical results. This leads to IV/GMM/OLS estimation of a dynamic linear probability model (LPM) with fixed effects. In this paper, I give a set of pros and cons of this procedure using explicit analytical results, some simulations, and an empirical application. I find that this procedure should be treated with caution, especially in fixed- T settings. In large-T settings, existing procedures cannot be directly applied. As a consequence, I give guidance as to what choices researchers should make in both these settings.</p> |
