Empirical economists are often deterred from the application of binary choice models with fixed effects mainly for two reasons: the incidental parameter bias and the computational challenge in (moderately ) large data sets. We show how both issues can be alleviated in the context of binary choice models with individual and time fixed effects . Thanks to several bias-corrections proposed by Fernandez-Val and Weidner (2016) , the incidental parameter bias can be reduced substantially. In order to make the estimation feasible even in panels with many fixed effects, we develop an efficient software routine, embedded in the R -package alpaca, that combines these corrections with an approach called method of alternating projections. Further, we contribute to the existing literature by conducting extensive simulation experiments  in large and even unbalanced panel settings. Finally, we estimate a dynamic probit model, to study the inter-temporal labor force participation of women in Germany .
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) estimators in 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 .
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binary choice models with fixed effects
nonlinear fixed effects models
R
bias
problem
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in (moderately ) large data sets. We show how both issues can be alleviated in the context
even in moderately large panels. Using the example
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. Thanks to several bias-corrections proposed by Fernandez-Val
, we show how both issues can be alleviated by combining the asymptotic bias corrections of Fern\'andez-Val
R
, the incidental parameter bias can be reduced substantially. In order to make the estimation feasible even in panels with many fixed effects, we develop an efficient software routine, embedded in the R -package alpaca, that combines these corrections with an approach called method of alternating projections. Further, we contribute to the existing literature by conducting
with an estimation algorithm proposed by Stammann (2018). We conduct
A
to analyze the statistical properties of various (bias-corrected) estimators
R
panel settings. Finally, we estimate a dynamic probit model, to study the inter-temporal labor force participation of women in Germany
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