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...

The economic shock induced by the pandemic has plunged European economies into a recession. Lockdowns and social distancing measures have affected economic life in a substantial way, with industries and population groups facing varying difficulties. This study explores potential future...

The Article Studies The Predictive Capabilities Of Qualitative Assessments Of Employment Expectations Obtained From Business Tendency Observations Of Entrepreneurial Activity, Which Are Currently A Widespread Source Of Economic Information Both In National And International Practice. The Study...

We evaluate nowcasts and one- to four-quarter-ahead forecasts of Swiss full time equivalent jobs from 1998q1–2011q4, comparing forecasts of the KOF Swiss Economic Institute and of the State Secretariat for Economic Affairs with the outcome of the reference series. Both forecasts are biased...

We examine rationality, forecasting accuracy, and economic value of the survey-based exchange rate forecasts for 10 developed and 23 developing countries at the 3-, 12-, and 24-month horizons. Using the data from two surveys for the period from 2004 to 2012, we find strong evidence that the...

This paper discusses methods to quantify risk and uncertainty in macroeconomic forecasts. Both, parametric and non-parametric procedures are developed. The former are based on a class of asymmetrically weighted normal distributions whereas the latter employ asymmetric bootstrap simulations. Both...

In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an...

The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection...

We study the forecasting performance of three alternative large scale approaches using a dataset for Germany that consists of 123 variables in quarterly frequency. These three approaches handle the dimensionality problem evoked by such a large dataset by aggregating information, yet on different...