Suchergebnisse

Approximation formulae are developed for the bias of ordinary andgeneralized Least Squares Dummy Variable (LSDV) estimators in dynamicpanel data models. Results from Kiviet (1995, 1999) are extended tohigher-order dynamic panel data models with general covariancestructure. The focus is on estimation of both short- and long-runcoefficients. The results show that proper modelling of thedisturbance covariance structure is indispensable. The biasapproximations are used to construct bias corrected estimators whichare then applied to quarterly data from 14 European Union countries.Money demand functions for M1, M2 and M3 are estimated for the EUarea as a whole for the period 1991:I-1995:IV. Significant spilloversbetween countries are found reflecting the dependence of domesticmoney demand on foreign developments. The empirical results show thatin general plausible long-run effects are obtained by the biascorrected estimators. Moreover, bias correction can be substantialunderlining the importance of more refined estimation techniques.Also the efficiency gains by exploiting the heteroscedasticity andcross-correlation patterns between countries are sometimesconsiderable.

The impact of industrial structure on local economic development is analyzed for the case of Morocco. Using annual data for 6 urban areas and 18 industrial sectors indicators for specialization, diversity and competiton of firms within a particular region are constructed for the years 1985-1995. The effects of these and other explanatory variables on local economic activity are estimated using a dynamic panel data model with both individual and time specific effects. The estimation results suggest significant positive specialization and diversity effects and significant negative competition effects asserting the importance of industrial structure for local economic development.