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A Partially Heterogeneous Framework for Analyzing Panel Data

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  • Vasilis Sarafidis
  • Neville Weber

Abstract

type="main" xml:id="obes12062-abs-0001"> This article proposes a partially heterogeneous framework for the analysis of panel data with fixed T. In particular, the population of cross-sectional units is grouped into clusters, such that slope parameter homogeneity is maintained only within clusters. Our method assumes no a priori information about the number of clusters and cluster membership and relies on the data instead. The unknown number of clusters and the corresponding partition are determined based on the concept of ‘partitional clustering’, using an information-based criterion. It is shown that this is strongly consistent, that is, it selects the true number of clusters with probability one as N→∞. Simulation experiments show that the proposed criterion performs well even with moderate N and the resulting parameter estimates are close to the true values. We apply the method in a panel data set of commercial banks in the US and we find five clusters, with significant differences in the slope parameters across clusters.

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  • Vasilis Sarafidis & Neville Weber, 2015. "A Partially Heterogeneous Framework for Analyzing Panel Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(2), pages 274-296, April.
    • Handle: RePEc:bla:obuest:v:77:y:2015:i:2:p:274-296
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    File URL: http://hdl.handle.net/10.1111/obes.2015.77.issue-2
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    Cited by:

    1. Boto-García, David & Mayor, Matías, 2022. "Domestic tourism and the resilience of hotel demand," Annals of Tourism Research, Elsevier, vol. 93(C).
    2. Kutlu, Levent & Sickles, Robin & Tsionas, Mike G., 2019. "Heterogeneous Decision-Making and Market Power," Working Papers 19-008, Rice University, Department of Economics.
    3. Vasilis Sarafidis & Tom Wansbeek, 2020. "Celebrating 40 Years of Panel Data Analysis: Past, Present and Future," Monash Econometrics and Business Statistics Working Papers 6/20, Monash University, Department of Econometrics and Business Statistics.
    4. Boyuan Zhang, 2022. "Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models," Papers 2211.16714, arXiv.org, revised Oct 2023.
    5. Liu, Ruiqi & Shang, Zuofeng & Zhang, Yonghui & Zhou, Qiankun, 2020. "Identification and estimation in panel models with overspecified number of groups," Journal of Econometrics, Elsevier, vol. 215(2), pages 574-590.
    6. Miao, Ke & Su, Liangjun & Wang, Wendun, 2020. "Panel threshold regressions with latent group structures," Journal of Econometrics, Elsevier, vol. 214(2), pages 451-481.
    7. Wang, Wuyi & Su, Liangjun, 2021. "Identifying latent group structures in nonlinear panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 272-295.
    8. Teemu Makkonen & Timo Mitze, 2019. "Deconstructing the Education-Innovation-Development Nexus in the EU-28 Using Panel Causality and Poolability Tests," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(2), pages 516-549, June.
    9. Artūras Juodis & Yiannis Karavias & Vasilis Sarafidis, 2021. "A homogeneous approach to testing for Granger non-causality in heterogeneous panels," Empirical Economics, Springer, vol. 60(1), pages 93-112, January.
    10. 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.
    11. Su, Liangjun & Ju, Gaosheng, 2018. "Identifying latent grouped patterns in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 554-573.
    12. Panagiotidis, Theodore & Printzis, Panagiotis, 2021. "Investment and uncertainty: Are large firms different from small ones?," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 302-317.
    13. Guowei Cui & Milda NorkutÄ— & Vasilis Sarafidis & Takashi Yamagata, 2022. "Two-stage instrumental variable estimation of linear panel data models with interactive effects [Eigenvalue ratio test for the number of factors]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 340-361.
    14. Jaqueson K. Galimberti, 2020. "Forecasting GDP Growth from Outer Space," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 697-722, August.
    15. Costantini, Mauro & Sousa, Ricardo M., 2022. "What uncertainty does to euro area sovereign bond markets: Flight to safety and flight to quality," Journal of International Money and Finance, Elsevier, vol. 122(C).
    16. Yiren Wang & Peter C B Phillips & Liangjun Su, 2023. "Panel Data Models with Time-Varying Latent Group Structures," Papers 2307.15863, arXiv.org.
    17. Levent Kutlu & Robin C. Sickles & Mike G. Tsionas & Emmanuel Mamatzakis, 2022. "Heterogeneous decision-making and market power: an application to Eurozone banks," Empirical Economics, Springer, vol. 63(6), pages 3061-3092, December.
    18. Abdul-Nasah Soale & Emmanuel Selorm Tsyawo, 2023. "Clustered Covariate Regression," Papers 2302.09255, arXiv.org, revised Jul 2023.
    19. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers CWP06/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Jaeram Lee & Jungjoon Ihm, 2022. "Heterogeneous Effects on Private Education Spending in Latent Groups in Korea," Asian Economic Journal, East Asian Economic Association, vol. 36(2), pages 159-179, June.
    21. Mitze, Timo & Naveed, Amjad & Ahmad, Nisar, 2016. "International, intersectoral, or unobservable? Measuring R&D spillovers under weak and strong cross-sectional dependence," Journal of Macroeconomics, Elsevier, vol. 50(C), pages 259-272.
    22. Mehrabani, Ali, 2023. "Estimation and identification of latent group structures in panel data," Journal of Econometrics, Elsevier, vol. 235(2), pages 1464-1482.
    23. Millimet, Daniel L. & Bellemare, Marc, 2023. "Fixed Effects and Causal Inference," IZA Discussion Papers 16202, Institute of Labor Economics (IZA).
    24. Panagiotidis, Theodore & Printzis, Panagiotis, 2021. "Investment and uncertainty: Are large firms different from small ones?," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 302-317.
    25. Daniel Czarnowske, 2022. "A Classifier-Lasso Approach for Estimating Production Functions with Latent Group Structures," Papers 2203.02220, arXiv.org.

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