Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288280 
Year of Publication: 
2020
Citation: 
[Journal:] Empirical Economics [ISSN:] 1435-8921 [Volume:] 60 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 327-351
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
In this paper, we compare alternative estimation approaches for factor augmented panel data models. Our focus lies on panel data sets where the number of panel groups (N) is large relative to the number of time periods (T). The principal component (PC) and common correlated effects (CCE) estimators were originally developed for panel data with large N and T, whereas the GMM approaches of Ahn et al. (J Econ 728 174:1–14, 2013) and Robertson and Sarafidis (J Econ 185(2):526–541, 2015) assume that T is small (that is T is fixed in the asymptotic analysis). Our comparison of existing methods addresses three different issues. First, we analyze the possibility of an inappropriate normalization of the factor space (the so-called normalization failure). In particular we propose a variant of the CCE estimator that avoids the normalization failure by adapting a weighting scheme inspired by the analysis of Mundlak (Econometrica 46(1):69–85, 1978). Second, we analyze the effects of estimating versus fixing the number of factors in advance. Third, we demonstrate how the design of the Monte Carlo simulations favors some estimators, which explains the conflicting findings from existing Monte Carlo experiments.
Subjects: 
Panel data
Interactive fixed effects
CCE estimator
GMM
JEL: 
C23
C38
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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