Economics Webinar - Best Linear and Quadratic Moments for Spatial Econometric Models

We provide a novel analytic procedure to construct best linear and quadratic moments of the generalized method of moments (GMM) estimation for a large class of network and spatial econometric models, which generate a GMM estimator that is asymptotically more efficient than the quasi maximum likelihood estimator when the disturbances are non-normal. We apply this procedure to a high order spatial autoregressive model with spatial errors, where the disturbances are heteroskedastic with an unknown distribution. We apply the model and the estimator to employment data in US counties, which demonstrates spatial inter-dependence patterns and channels of regional economic growth.