On the Role of the Rank Condition in CCE Estimation of Factor-Augmented Panel Regressions

Research output: Contribution to journalArticle

Abstract

A popular approach to factor-augmented panel regressions is the common correlatedeffects (CCE) estimator of Pesaran (Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012, 2006). This paper points to a problem with the CCE approach that appears in the empirically relevant case when the number of factors is strictly less than the number of observables used in their estimation. Specifically, the use of too many observables causes the second moment matrix of the estimated factors to become asymptotically singular, an issue that has not yet been appropriately accounted for. The purpose of the present paper is to fill this gap in the literature.

Details

Authors
Organisations
External organisations
  • University of Amsterdam
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Economics and Business

Keywords

  • Factor-augmented panel regression, CCE estimation, Moore–Penrose inverse
Original languageEnglish
Pages (from-to)60-64
Number of pages5
JournalJournal of Econometrics
Volume197
Issue number1
Early online date2016 Oct 28
Publication statusPublished - 2017 Mar
Publication categoryResearch
Peer-reviewedYes

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