Robust Estimation of Linear Fixed Effects Panel Data Models with an Application to the Exporter Productivity Premium
Vincenzo Verardi and
Joachim Wagner ()
No 4928, IZA Discussion Papers from Institute of Labor Economics (IZA)
Abstract:
In empirical studies it often happens that some variables for some units are far away from the other observations in the sample. These extreme observations, or outliers, often have a large impact on the results of statistical analyses – conclusions based on a sample with and without these units may differ drastically. While applied researchers tend to be aware of this, the detection of outliers and their appropriate treatment is often dealt with in a rather sloppy manner. One reason for this habit seems to be the lack of availability of appropriate canned programs for robust methods that can be used in the presence of outliers. Our paper intents to improve on this situation by presenting a highly robust method for estimation of the popular linear fixed effects panel data model, and to supply Stata code for it. In an application from the field of the micro-econometrics of international firm activities we demonstrate that outliers can indeed drive results.
Keywords: robust estimation; panel data; outliers; Stata; exporter productivity premium (search for similar items in EconPapers)
JEL-codes: C23 C81 C87 F14 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2010-05
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (8)
Published - published in: Jahrbücher für Nationalökonomie und Statistik, 2011, 231 (4), 546-557
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Journal Article: Robust Estimation of Linear Fixed Effects Panel Data Models with an Application to the Exporter Productivity Premium (2011) 
Working Paper: Robust estimation of linear fixed effects panel data models with an application to the exporter productivity premium (2011)
Working Paper: Robust Estimation of Linear Fixed Effects Panel Data Models with an Application to the Exporter Productivity Premium (2010) 
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