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Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks

Author

Listed:
  • Degui Li

    (University of York)

  • Junhui Qian

    (Shanghai Jiao Tong University)

  • Su Liangjun

    (Singapore Management University)

Abstract

In this paper we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. 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. 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.

Suggested Citation

  • Degui Li & Junhui Qian & Su Liangjun, 2015. "Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks," Working Papers 12-2015, Singapore Management University, School of Economics.
    • Handle: RePEc:siu:wpaper:12-2015
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    File URL: http://ink.library.smu.edu.sg/soe_research/1746/
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    Cited by:

    1. Saptorshee Kanto Chakraborty & Massimiliano Mazzanti, 2021. "Revisiting the literature on the dynamic Environmental Kuznets Curves using a latent structure approach," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(3), pages 923-941, October.
    2. Jafari-Sadeghi, Vahid & Garcia-Perez, Alexeis & Candelo, Elena & Couturier, Jerome, 2021. "Exploring the impact of digital transformation on technology entrepreneurship and technological market expansion: The role of technology readiness, exploration and exploitation," Journal of Business Research, Elsevier, vol. 124(C), pages 100-111.
    3. Bin Peng & Liangjun Su & Joakim Westerlund & Yanrong Yang, 2021. "Interactive Effects Panel Data Models with General Factors and Regressors," Papers 2111.11506, arXiv.org.
    4. Kock, Anders Bredahl, 2016. "Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models," Journal of Econometrics, Elsevier, vol. 195(1), pages 71-85.
    5. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
    6. Yufeng Mao & Bin Peng & Mervyn Silvapulle & Param Silvapulle & Yanrong Yang, 2021. "Decomposition of Bilateral Trade Flows Using a Three-Dimensional Panel Data Model," Papers 2101.06805, arXiv.org.
    7. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    8. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.

    More about this item

    Keywords

    Change point; Interactive fixed effects; LASSO; Panel data; Penalized estimation; Principal component analysis.;
    All these keywords.

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