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Change Point Estimation in Panel Data with Time-Varying Individual Effects

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  • Otilia Boldea
  • Bettina Drepper
  • Zhuojiong Gan

Abstract

This paper proposes a method for estimating multiple change points in panel data models with unobserved individual effects via ordinary least-squares (OLS). Typically, in this setting, the OLS slope estimators are inconsistent due to the unobserved individual effects bias. As a consequence, existing methods remove the individual effects before change point estimation through data transformations such as first-differencing. We prove that under reasonable assumptions, the unobserved individual effects bias has no impact on the consistent estimation of change points. Our simulations show that since our method does not remove any variation in the dataset before change point estimation, it performs better in small samples compared to first-differencing methods. We focus on short panels because they are commonly used in practice, and allow for the unobserved individual effects to vary over time. Our method is illustrated via two applications: the environmental Kuznets curve and the U.S. house price expectations after the financial crisis.

Suggested Citation

  • Otilia Boldea & Bettina Drepper & Zhuojiong Gan, 2018. "Change Point Estimation in Panel Data with Time-Varying Individual Effects," Papers 1808.03109, arXiv.org.
  • Handle: RePEc:arx:papers:1808.03109
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    1. Luis Armona & Andreas Fuster & Basit Zafar, 2019. "Home Price Expectations and Behaviour: Evidence from a Randomized Information Experiment," Review of Economic Studies, Oxford University Press, vol. 86(4), pages 1371-1410.
    2. Zhongjun Qu & Pierre Perron, 2007. "Estimating and Testing Structural Changes in Multivariate Regressions," Econometrica, Econometric Society, vol. 75(2), pages 459-502, March.
    3. Arouri, Mohamed El Hedi & Ben Youssef, Adel & M'henni, Hatem & Rault, Christophe, 2012. "Energy consumption, economic growth and CO2 emissions in Middle East and North African countries," Energy Policy, Elsevier, vol. 45(C), pages 342-349.
    4. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    5. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    6. Hyungsik Roger Moon & Martin Weidner, 2015. "Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects," Econometrica, Econometric Society, vol. 83(4), pages 1543-1579, July.
    7. Lu, Xun & Su, Liangjun, 2016. "Shrinkage estimation of dynamic panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 190(1), pages 148-175.
    8. Lean, Hooi Hooi & Smyth, Russell, 2010. "CO2 emissions, electricity consumption and output in ASEAN," Applied Energy, Elsevier, vol. 87(6), pages 1858-1864, June.
    9. Apergis, Nicholas & Payne, James E., 2009. "CO2 emissions, energy usage, and output in Central America," Energy Policy, Elsevier, vol. 37(8), pages 3282-3286, August.
    10. Farhani, Sahbi & Mrizak, Sana & Chaibi, Anissa & Rault, Christophe, 2014. "The environmental Kuznets curve and sustainability: A panel data analysis," Energy Policy, Elsevier, vol. 71(C), pages 189-198.
    11. Badi H. Baltagi & Chihwa Kao & Long Liu, 2017. "Estimation and identification of change points in panel models with nonstationary or stationary regressors and error term," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 85-102, March.
    12. Margaret Kyle & Heidi Williams, 2017. "Is American Health Care Uniquely Inefficient? Evidence from Prescription Drugs," American Economic Review, American Economic Association, vol. 107(5), pages 486-490, May.
    13. Harchaoui, Z. & Lévy-Leduc, C., 2010. "Multiple Change-Point Estimation With a Total Variation Penalty," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1480-1493.
    14. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    15. De Wachter, Stefan & Tzavalis, Elias, 2012. "Detection of structural breaks in linear dynamic panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3020-3034.
    16. Niu, Geng & van Soest, Arthur, 2014. "House Price Expectations," IZA Discussion Papers 8536, Institute of Labor Economics (IZA).
    17. 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.
    18. Baier, Scott L. & Bergstrand, Jeffrey H., 2007. "Do free trade agreements actually increase members' international trade?," Journal of International Economics, Elsevier, vol. 71(1), pages 72-95, March.
    19. Degui Li & Junhui Qian & Liangjun Su, 2016. "Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1804-1819, October.
    20. Alexander Aue & Lajos Horváth, 2013. "Structural breaks in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 1-16, January.
    21. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.
    22. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
    23. Pierre Perron & Yohei Yamamoto, 2015. "Using OLS to Estimate and Test for Structural Changes in Models with Endogenous Regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 119-144, January.
    24. Sahbi Farhani & Sana Mrizak & Anissa Chaibi & Christophe Rault, 2014. "The Environmental Kuznets Curve and Sustainability: A Panel Data Analysis," CESifo Working Paper Series 4787, CESifo.
    25. Haeran Cho & Piotr Fryzlewicz, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 475-507, March.
    26. Ngai Hang Chan & Chun Yip Yau & Rong-Mao Zhang, 2014. "Group LASSO for Structural Break Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 590-599, June.
    27. Luisa Blanco & Isabel Ruiz, 2013. "The Impact of Crime and Insecurity on Trust in Democracy and Institutions," American Economic Review, American Economic Association, vol. 103(3), pages 284-288, May.
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