Inference in Unbalanced Panel Data Models with Interactive Fixed Effects

Fuente: arXiv
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Autores principales: Czarnowske, Daniel, Stammann, Amrei
Formato: Preprint
Publicado: 2020
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author Czarnowske, Daniel
Stammann, Amrei
author_facet Czarnowske, Daniel
Stammann, Amrei
contents We derive the asymptotic theory of Bai (2009)'s interactive fixed effects estimator for unbalanced panels in which the source of attrition is conditionally random. For inference, we propose a method of alternating projections algorithm based on straightforward scalar expressions to compute the residualized variables required for bias correction and covariance matrix estimation. Simulation experiments confirm that our asymptotic results provide reliable finite-sample approximations. We also reassess Acemoglu et al. (2019). Allowing for a more general form of unobserved heterogeneity, we confirm significant effects of democratization on economic growth.
format Preprint
id arxiv_https___arxiv_org_abs_2004_03414
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Inference in Unbalanced Panel Data Models with Interactive Fixed Effects
Czarnowske, Daniel
Stammann, Amrei
Econometrics
We derive the asymptotic theory of Bai (2009)'s interactive fixed effects estimator for unbalanced panels in which the source of attrition is conditionally random. For inference, we propose a method of alternating projections algorithm based on straightforward scalar expressions to compute the residualized variables required for bias correction and covariance matrix estimation. Simulation experiments confirm that our asymptotic results provide reliable finite-sample approximations. We also reassess Acemoglu et al. (2019). Allowing for a more general form of unobserved heterogeneity, we confirm significant effects of democratization on economic growth.
title Inference in Unbalanced Panel Data Models with Interactive Fixed Effects
topic Econometrics
url https://arxiv.org/abs/2004.03414