Heteroskedasticity and Serial Correlation in the Error Component Model

Abstract

The standard error component model given by Eqs. ( 2.1) and ( 2.2) assumes that the regression disturbances are homoskedastic with the same variance across time and individuals. This may be a restrictive assumption for panels, where the cross-sectional units may be of varying size and as a result may exhibit different variations. For example, when dealing with gasoline demand across OECD countries, steam electric generation across various size utilities, or estimating cost functions for various US airline firms, one should expect to find heteroskedasticity in the disturbance term. Assuming homoskedastic disturbances when heteroskedasticity is present will still result in consistent estimates of the regression coefficients, but these estimates will not be efficient. Also, the standard errors of these estimates will be biased and one should compute robust standard errors correcting for the possible presence of heteroskedasticity.