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Estimation and Inference in Panel Structure Models

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Author Info
Yixiao Sun (University of California, San Diego)
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.

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Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number 2005-11.

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Date of creation: 01 Oct 2005
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Handle: RePEc:cdl:ucsdec:2005-11

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Keywords: dynamic panel data model; group structure; logistic regression; nonregular test; parameter heterogeneity;

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  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. [Downloadable!]
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