The development of employment and unemployment in regional labour markets is known to spatially interdependent. Global Vector-Autoregressive (GVAR) models generate a link between the local and the surrounding labour markets and thus might be useful when analysing and forecasting employment and...

Die Arbeitslosigkeit für die 176 deutschen administrativen Arbeitsmarktregionen (im Allgemeinen Arbeitsagenturbezirke) wird auf einer monatlichen Basis prognostiziert. Wegen ihrer geringen Größe existieren zwischen diesen regionalen Einheiten starke räumliche Interdependenzen. Um diese und...

Problems associated with employment have become a major issue in regional economics, especially in those countries, such as Spain, where unemployment represents a serious threat to regional stability. In this context, the purpose of our paper is twofold: first, to develop a prediction instrument...

We propose using sign restrictions to identify regional labor demand shocks in a panel VAR of US federal states. Observed migration responds significantly, but less persistently than the residual-based migration measure constructed by Blanchard and Katz (1992)

In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden- Württemberg) and Eastern Germany. We overcome the problem of a...

This paper aims to compare the performance of different Artificial Neural Networks techniques for tourist demand forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis function and an Elman network. We also evaluate the...

This study compares the performance of different Artificial Neural Networks models for tourist demand forecasting in a multiple-output framework. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron network, a radial basis function network and an...

I evaluate whether incorporating sub-national trends improves macroeconomic fore-casting accuracy in a deep machine learning framework. Specifically, I adopt a computer vision setting by transforming U.S. economic data into a ‘video’ series of geographic ‘images’ and utilizing a...

We consider a logistic transform of the monthly US unemployment rate. For this time series, a pseudo out-of-sample forecasting competition is held between linear and nonlinear models and averages of these models. To combine predictive densities, we use two complementary methods: Bayesian model...

Empirical assessments of the forecasting power of spatial panel data econometric models are still scarcely available. Moreover, several methodological contributions rely on simulated data to showcase the potential of proposed methods. While simulations may be useful to evaluate the properties of...