Empirical economists are often deterred from the application of nonlinear fixed effects models mainly for two reasons: the incidental parameter problem and the computational challenge even in moderately large panels. Using the example of binary choice models with individual and time fixed effects, we show how both issues can be alleviated by combining the asymptotic bias corrections of Fern\'andez-Val and Weidner (2016) with an estimation algorithm proposed by Stammann (2018). We conduct extensive simulation experiments to analyze the statistical properties of various ( bias-corrected) estimatorsin large and even unbalanced panels. Our simulation results provide new insights that are relevant for the application of bias corrections in empirical work. Overall, we find that analytically bias corrected estimators are clearly preferable, especially for unbalanced panels.
Empirical economists are often deterred from the application of fixed effects binary choice models mainly for two reasons: the incidental parameter problem and the computational challenge even in moderately large panels. Using the example of binary choice models with individual and time fixed effects, we show how both issues can be alleviated by combining asymptotic bias corrections with computational advances. Because unbalancedness is often encountered in applied work, we investigate its consequences on the finite sample properties of various ( bias corrected) estimators. In simulation experiments we find that analytical bias corrections perform particularly well, whereas split-panel jackknife estimators can be severely biased in unbalanced panels.
R
nonlinear fixed effects
fixed effects binary choice
D
the
R
of Fern\'andez-Val and Weidner (2016) with an estimation algorithm proposed by Stammann (2018). We conduct extensive simulation experiments to analyze the statistical
with computational advances. Because unbalancedness is often encountered in applied work, we investigate its consequences on the finite sample
R
bias-corrected) estimatorsin large and even unbalanced panels. Our simulation results provide new insights that are relevant for the application of bias corrections in empirical work. Overall,
bias corrected) estimators. In simulation experiments
R
analytically bias corrected estimators are clearly preferable, especially for
analytical bias corrections perform particularly well, whereas split-panel jackknife estimators can be severely biased in