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.
Highlights
The standard error component panel data model assumes that the disturbances have homoskedastic variances and constant serial correlation through the random individual effects, see Hsiao (2003) and Baltagi (2005)
And Gardiol (2000), for example, derived an Lagrange Multiplier (LM) statistic which tests for homoskedasticity of the disturbances in the context of a oneway random effects panel data model
This paper extends the Holly and Gardiol (2000) model to allow for first order serial correlation in the remainder disturbances as described in Baltagi and Li (1995)
Summary
The standard error component panel data model assumes that the disturbances have homoskedastic variances and constant serial correlation through the random individual effects, see Hsiao (2003) and Baltagi (2005). While, Holly and Gardiol (2000), for example, derived an LM statistic which tests for homoskedasticity of the disturbances in the context of a oneway random effects panel data model. The latter LM test assumes no serial correlation in the remainder disturbances. This paper extends the Holly and Gardiol (2000) model to allow for first order serial correlation in the remainder disturbances as described in Baltagi and Li (1995) It derives a joint LM test for homoskedasticity and no first order serial correlation. 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
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