TY - JOUR
T1 - Specification of variance matrices for panel data models
AU - Magnus, Jan R.
AU - Muris, Chris
PY - 2010/2/1
Y1 - 2010/2/1
N2 - 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 household- or station-specific autocorrelation, and for time-specific spatial correlation.
AB - 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 household- or station-specific autocorrelation, and for time-specific spatial correlation.
UR - http://www.scopus.com/inward/record.url?scp=77951136783&partnerID=8YFLogxK
U2 - 10.1017/S0266466609090756
DO - 10.1017/S0266466609090756
M3 - Article (Academic Journal)
AN - SCOPUS:77951136783
SN - 0266-4666
VL - 26
SP - 301
EP - 310
JO - Econometric Theory
JF - Econometric Theory
IS - 1
ER -