Binary Choice Models with High-Dimensional Individual and Time Fixed Effects
Daniel Czarnowske and
Papers from arXiv.org
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.
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