This paper focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006), a dynamic spatial GMM estimator is proposed...

This paper estimates individual wage equations in order to test two rival non-nested theories of economic agglomeration, namely New Economic Geography (NEG), as represented by the NEG wage equation and urban economic (UE) theory, in which wages relate to employment density. The paper makes an...

This paper develops consistency and asymptotic normality of parameter estimates for a higher-order spatial autoregressive model whose order, and number of regressors, are allowed to approach infinity slowly with sample size. Both least squares and instrumental variables estimates are examined,...

In a panel data model with fixed effects, possible cross-sectional dependence is investigated in a spatial autoregressive setting. An Edgeworth expansion is developed for the maximum likelihood estimate of the spatial correlation coefficient. The expansion is used to develop more accurate...

This study uses a longitudinal data source to study the effects of conflict-induced displacement on labour market outcomes for Bosnians in post-war Bosnia and Herzegovina. To account for endogeneity in the displacement status, I exploit the fact that the level of violence in the pre-war...

For testing lack of correlation against spatial autoregressive alternatives, Lagrange multiplier tests enjoy their usual computational advantages, but the (χ2) first-order asymptotic approximation to critical values can be poor in small samples. We develop refined tests for lack of spatial...

We consider testing the null hypothesis of no spatial autocorrelation against the alternative of first order spatial autoregression. A Wald test statistic has good first order asymptotic properties, but these may not be relevant in small or moderate-sized samples, especially as (depending on...