Asymptotic distribution of factor augmented estimators for panel regression

Ryan Greenaway-McGrevy, Chirok Han, Donggyu Sul

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)


In this paper we derive an asymptotic theory for linear panel regression augmented with estimated common factors. We give conditions under which the estimated factors can be used in place of the latent factors in the regression equation. For the principal components estimate of the factor space it is shown that these conditions are satisfied when TN→0 and N T3→0 under regularity. Monte Carlo studies verify the asymptotic theory.

Original languageEnglish
Pages (from-to)48-53
Number of pages6
JournalJournal of Econometrics
Issue number1
Publication statusPublished - 2012 Jul

Bibliographical note

Funding Information:
The views expressed herein are those of the authors and not necessarily those of the Bureau of Economic Analysis or the Department of Commerce. We thank I. Choi, Sainan Jin, C.J. Kim, H. R. Moon, Liangjun Su, K. Tanaka, Y. J. Whang, the editors and anonymous referees for helpful comments. A previous version of this paper was circulated under the title “Estimating and Testing Idiosyncratic Equations using Cross Section Dependent Panel Data”. Research for the paper was supported under a Marsden grant.


  • Cross section dependence
  • Factor augmented estimator
  • Factor augmented panel regression
  • Interactive fixed effects
  • Principal component augmented estimator

ASJC Scopus subject areas

  • Economics and Econometrics


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