conduct a pseudo out-of-sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find … conventional indicators in a monthly forecasting experiment. Exploiting the high-frequency nature of the data, we find that the …

-of-sample predictions and forecasting. We identify economic drivers of our machine learning models using a novel framework based on Shapley …

. Nevertheless, their forecasting properties are still barely explored. We fill this gap by comparing the quality of real … priors from a DSGE model. We show that the analyzed DSGE model is relatively successful in forecasting the US economy in the … accurate forecasting power of the DSGE turns out to be similar or better than that of the SPF for all the variables and …

We investigate the potential for statistical forecasting of aggregate oil and gas investment on the Norwegian …

This paper applies component-wise boosting to the topic of regional economic forecasting. Component-wise boosting is a … pre-selection algorithm of indicators for forecasting. By using unique quarterly real gross domestic product data for two … economic forecasting. We additionally take a closer look into the algorithm and ask which indicators get selected. All in all …

choice of data transformation. - Aggregation ; Forecasting ; Inflation …

This paper looks into the 'fine print' of boosting for economic forecasting. By using German industrial production for … selected by the boosting algorithm over time and four different forecasting horizons. It turns out that a number of hard … therefore important to forecasting the performance of the German economy. However, there are indicators such as money supply …