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The author considers a panel vector-autoregressive model with cross-sectional dependence of the disturbances characterized by a spatial autoregressive process. The author proposes 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. The author compares the small-sample performance of various estimation strategies in a Monte Carlo study.
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