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...

In this paper, I propose an instrumental variable (IV) estimation procedure to estimate global VAR (GVAR) models and show that it leads to consistent and asymptotically normal estimates of the parameters. I also provide computationally simple conditions that guarantee that the GVAR model is...

We test a New Economic Geography (NEG) model for U.S. counties, employing a new strategy that allows us to bring the full NEG model to the data, and to assess selected elements of this model separately. We find no empirical support for the full NEG model. Regional wages in the U.S. do not...

We study dynamic panel data models where the long run outcome for a particular cross-section is affected by a weighted average of the outcomes in the other cross-sections. We show that imposing such a structure implies several cointegrating relationships that are nonlinear in the coefficients to...

This paper studies the spatial random effects and spatial fixed effects model. The model includes a Cliff and Ord type spatial lag of the dependent variable as well as a spatially lagged one-way error component structure, accounting for both heterogeneity and spatial correlation across units. We...

We test a New Economic Geography (NEG) model for U.S. counties, employing a new strategy that allows us to bring the full NEG model to the data, and to assess selected elements of this model separately. We find no empirical support for the full NEG model. Regional wages in the U.S. do not...