Common correlated effects estimation of heterogeneous dynamic panel quantile regression models

Matthew Harding, Carlos Lamarche, M. Hashem Pesaran

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed in the literature and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. The new quantile regression estimator is shown to be consistent and its asymptotic distribution is derived. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time-of-Use pricing using a large randomized control trial.

Original languageEnglish
Pages (from-to)294-314
Number of pages21
JournalJournal of Applied Econometrics
Issue number3
StatePublished - Apr 1 2020

Bibliographical note

Funding Information:
We would like to thank Alexander Chudik, Ron Smith, and Jeff Wooldridge for helpful comments and suggestions as well as seminar participants at Michigan State University, University of California at Irvine, University of Glasgow, University of Miami, Universidad Di Tella, University of Oklahoma, Universidad de San Andres, University of Waterloo, Central Bank of Argentina, the 2017 International Association for Applied Econometrics conference, and the 2017 Boneyard Econometrics conference in celebration of Professor Roger Koenker.

Publisher Copyright:
© 2020 John Wiley & Sons, Ltd.

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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