Abstract :
Many regression models have two dimensions, say time (t = 1, . . . ,T ) and households
(i = 1, . . . , N), as in panel data, error components, or spatial econometrics. In
estimating such models we need to specify the structure of the error variance matrix
Ω, which is of dimension T N ×T N. If T N is large, then direct computation of the
determinant and inverse of Ω is not practical. In this note we define structures of
Ω that allow the computation of its determinant and inverse, only using matrices of
orders T and N, and at the same time allowing for heteroskedasticity, for householdor
station-specific autocorrelation, and for time-specific spatial correlation.
