Testing for heteroskedasticity and spatial correlation in a random effects panel data model

  • Authors:

  • Badi H. Baltagi;Seuck Heun Song;Jae Hyeok Kwon

  • Affiliations:

  • Department of Economics and Center for Policy Research, Syracuse University, Syracuse, NY 13244-1020, USA;Department of Statistics, Korea University, Sungbuk-Ku, Seoul, 136-701, Republic of Korea;Department of Statistics, Korea University, Sungbuk-Ku, Seoul, 136-701, Republic of Korea

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2009

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Abstract

A panel data regression model with heteroskedastic as well as spatially correlated disturbances is considered, and a joint LM test for homoskedasticity and no spatial correlation is derived. In addition, a conditional LM test for no spatial correlation given heteroskedasticity, as well as a conditional LM test for homoskedasticity given spatial correlation, are also derived. These LM tests are compared with marginal LM tests that ignore heteroskedasticity in testing for spatial correlation, or spatial correlation in testing for homoskedasticity. Monte Carlo results show that these LM tests, as well as their LR counterparts, perform well, even for small N and T. However, misleading inferences can occur when using marginal, rather than joint or conditional LM tests when spatial correlation or heteroskedasticity is present.