It is well known that the standard Breusch and Pagan (1980) LM test for cross-equation correlation in a SUR model is not appropriate for testing cross-sectional dependence in panel data models when the number of cross-sectional units (n) is large and the number of time periods (T) is small. In...

This paper considers the problem of estimation and forecasting in a panel data model with random individual effects and AR(p) remainder disturbances. It utilizes a simple exact transformation for the AR(p) time series process derived by Baltagi and Li (1994) and obtains the generalized least...

A wage curve is a decreasing function of wages on the regional unemployment rate. Most empirical studies on the wage curve ignore possible spatial interaction effects between the regions which are the primary units of research. This paper reconsiders the western German wage curve with a special...

This paper considers the estimation of a linear regression involving the spatial autoregressive (SAR) error term, which is nearly nonstationary. The asymptotics properties of the ordinary least squares (OLS), true generalized least squares (GLS) and feasible generalized least squares (FGLS)...

This paper considers the problem of estimation and forecasting in a panel data model with random individual effects and AR(p) remainder disturbances. It utilizes a simple exact transformation for the AR(p) time series process derived by Baltagi and Li (1994) and obtains the generalized least...

Various forecasts using panel data with spatial error correlation are compared using Monte Carlo experiments. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best...

This paper proposes a generalized panel data model with random effects and first-order spatially autocorrelated residuals that encompasses two previously suggested specifications. The first one is described in Anselin’s (1988) book and the second one by Kapoor, Kelejian, and Prucha (2007). Our...

Following Arnold and Wied (2010), we suggest an improved generalized moments estimator for the spatial moving average error model which takes explicitly into account that the moment conditions are based on OLS residuals rather than the true disturbances.

This paper modifies the Hausman and Taylor (1981) panel data estimator to allow for serial correlation in the remainder disturbances. It demonstrates the gains in efficiency of this estimator versus the standard panel data estimators that ignore serial correlation using Monte Carlo experiments.

The standard LM tests for spatial dependence in linear and panel regressions are derived under the normality and homoskedasticity assumptions of the regression disturbances. Hence, they may not be robust against non-normality or heteroskedasticity of the disturbances. Following Born and Breitung...