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Estimation and Inference in Panel Structure Models

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  • Sun, Yixiao X

Abstract

This paper proposes and implements a tractable approach to detect group structure in panel data. The mechanism works by means of a panel structure model, which assumes that individuals form a number of homogeneous groups in a heterogeneous population. Within each group, the (linear) regression coe¢ cients are the same, while they may be different across different groups. The econometrician is not presumed to know the group structure. Instead, a multinomial logistic regression is used to infer which individuals belong to which groups. The model is estimated via maximum likelihood. We prove the consistency and asymptotic normality of a global MLE under the mild assumption that the time dimension is larger than the number of regressors in the linear regression. We propose a likelihood ratio test to test the null of one group against the alternative of multiple groups. Simulation studies show that the MLE performs quite well and the likelihood ratio test has good size and power properties in finite samples.

Suggested Citation

  • Sun, Yixiao X, 2005. "Estimation and Inference in Panel Structure Models," University of California at San Diego, Economics Working Paper Series qt5tf1231k, Department of Economics, UC San Diego.
  • Handle: RePEc:cdl:ucsdec:qt5tf1231k
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    References listed on IDEAS

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    Cited by:

    1. Andrew Grodner & Thomas Kniesner, 2005. "Labor Supply with Social Interactions: Econometric Estimates and Their Tax Policy Implications," Center for Policy Research Working Papers 69, Center for Policy Research, Maxwell School, Syracuse University.
    2. Tomohiro Ando & Jushan Bai, 2016. "Panel Data Models with Grouped Factor Structure Under Unknown Group Membership," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 163-191, January.
    3. Bester, C. Alan & Hansen, Christian B., 2016. "Grouped effects estimators in fixed effects models," Journal of Econometrics, Elsevier, vol. 190(1), pages 197-208.

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