Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 237
I consider a panel vector-autoregressive model with cross-sectional dependence of the disturbances characterized by a spatial autoregressive process. I propose a three-step estimation procedure. Its first step is an instrumental variable estimation that ignores the spatial correlation. In the second step, the estimated disturbances are used in a multivariate spatial generalized moments estimation to infer the degree of spatial correlation. The final step of the procedure uses transformed data and applies standard techniques for estimation of panel vector-autoregressive models. I compare the small-sample performance of various estimation strategies in a Monte Carlo study.
spatial PVAR multivariate dynamic panel data model spatial GM spatial Cochrane-Orcutt transformation constrained maximum likelihood estimation