Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks

Degui Li, Junhui Qian, Liangjun Su

Research output: Contribution to journalArticlepeer-review


In this paper we consider estimation of common structural breaks in panel data models with interactive fixed effects which are unobservable. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. The Monte Carlo simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data.
Original languageEnglish
Pages (from-to)1804-1819
Number of pages16
JournalJournal of the American Statistical Association
Issue number516
Early online date22 Dec 2015
Publication statusPublished - 4 Jan 2017

Bibliographical note

This is an author produced version of a paper published in Journal of the American Statistical Association. Uploaded in accordance with the publisher's self-archiving policy.


  • Change point
  • Interactive fixed effects
  • Panel data
  • Penalized estimation
  • Principal component analysis

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