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Full-Text Articles in Social and Behavioral Sciences
A Dynamic Spatial Panel Data Approach To The German Wage Curve, Badi H. Baltagi, Uwe Blien, Katja Wolf
A Dynamic Spatial Panel Data Approach To The German Wage Curve, Badi H. Baltagi, Uwe Blien, Katja Wolf
Center for Policy Research
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 focus on the geography of labour markets. Spillovers between regions are taken into account. The paper tests whether the unemployment rate in the larger surrounding region also affects wages. In addition, agglomeration effects and effects of local monopsony are assessed. The main data base is a random sample of 974,179 employees observed over …
Seemingly Unrelated Regressions With Spatial Error Components, Badi H. Baltagi, Alain Pirotte
Seemingly Unrelated Regressions With Spatial Error Components, Badi H. Baltagi, Alain Pirotte
Center for Policy Research
This paper considers various estimators using panel data seemingly unrelated regressions (SUR) with spatial error correlation. The true data generating process is assumed to be SUR with spatial error of the autoregressive or moving average type. Moreover, the remainder term of the spatial process is assumed to follow an error component structure. Both maximum likelihood and generalized moments (GM) methods of estimation are used. Using Monte Carlo experiments, we check the performance of these estimators and their forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous versus homogeneous panel data models.
Panel Data Inference Under Spatial Dependence, Badi H. Baltagi, Alain Pirotte
Panel Data Inference Under Spatial Dependence, Badi H. Baltagi, Alain Pirotte
Center for Policy Research
This paper focuses on inference based on the usual panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial auto-regressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the usual panel data …