System of panel models are popular models in applied sciences and the question of spatial errors has created the recent demand for spatial system estimation of panel models. Therefore we propose new diagnostic methods to explore if the spatial component will change significantly the outcome of...

System of panel models are popular models in applied sciences and the question of spatial errors has created the recent demand for spatial system estimation of panel models. Therefore we propose new diagnostic methods to explore if the spatial component will change significantly the outcome of...

Spatial autoregressive models come with a variety of estimators and it is interesting and useful to compare the estimators by location and covariance properties. In this paper, we first study the local sensitivity behavior of the main least squares estimator by using matrix derivatives. We then...

We analyze the influence of newly constructed globalization measures on regional growth for the EU-27 countries between 2001 and 2006. The spatial Chow-Lin procedure, a method constructed by the authors, was used to construct on a NUTS-2 level a complete regional data for exports, imports and...

The Hodrick-Prescott (HP) method was originally developed to smooth time series, i.e. to get a smooth (long-term) component. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior for the smoothness component. Extending this Bayesian approach in a linear model...

The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term component of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior using differencing matrices for the...

The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a longterm component of stationary series like growth rates. The new extended HP smoothing model is applied to data-sets with an underlying metric and requires a Bayesian linear regression model with a...

Beer sales in Germany are confronted for several years with a shrinking market share in the market of alcoholic beverages. I use the approach of sales response function (SRF) models as in Polasek and Baier (2010) and adapt it to time series observation of beer sales for simultaneous estimation....

Missing data in dynamic panel models occur quite often since detailed recording of the dependent variable is often not possible at all observation points in time and space. In this paper we develop classical and Bayesian methods to complete missing data in panel models. The Chow-Lin (1971)...

The mean square error (MSE) compares point forecasts or a location parameter of the forecasting distribution with actual observations by the quadratic loss criterion. This paper shows how the Theil decomposition of the MSE error into a bias, variance and noise component which was proposed for...