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Testing for Heteroskedasticity and Serial Correlation in a Random Effects Panel Data Model

Badi Baltagi (), Byoung Cheol Jung and Seuck Heun Song

No 111, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: This paper considers a panel data regression model with heteroskedastic as well as serially correlated disturbances, and derives a joint LM test for homoskedasticity and no first order serial correlation. The restricted model is the standard random individual error component model. It also derives a conditional LM test for homoskedasticity given serial correlation, as well as a conditional LM test for no first order serial correlation given heteroskedasticity, all in the context of a random effects panel data model. Monte Carlo results show that these tests, along with their likelihood ratio alternatives, have good size and power under various forms of heteroskedasticity including exponential and quadratic functional forms.

Keywords: Panel data; heteroskedasticity; serial correlation; Lagrange Multiplier tests; likelihood ratio; random effects (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ore
Date: 2008-12
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Journal Article: Testing for heteroskedasticity and serial correlation in a random effects panel data model (2010) Downloads
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