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 loss function of 15 European governments as implied by their budget balance forecasts. Results suggest that the shape of the loss function varies across countries. The loss function becomes more asymmetric as the forecast horizon increases and in advance of parliamentary election....

Including disaggregate variables or using information extracted from the disaggregate variables into a forecasting model for an eco- nomic aggregate may improve the forecasting accuracy. In this paper we suggest to use boosting as a method to select the disaggregate variables which are most...

This study analyzes the performance of the IMF World Economic Outlook forecasts for world output and the aggregates of both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current and the next year, we examine whether IMF forecasts can be...

The interest rate assumptions for macroeconomic forecasts differ considerably among central banks. Common approaches are given by the assumption of constant interest rates, interest rates expected by market participants, or the central bank's own interest rate expectations. From a theoretical...

This paper assesses the relative performance of central bank staff forecasts and of private forecasters for inflation and output. We show that the Federal Reserve (Fed), and less so the European Central Bank (ECB), has a significant information advantage concerning inflation and output...

We examine the behavior of forecasts for real GDP growth using a large panel of individual forecasts from 30 advanced and emerging economies during 1989-2010. Our main findings are as follows. First, our evidence does not support the validity of the sticky information model (Mankiw and Reis,...

We study the forecasting performance of three alternative large scale approaches for German key macroeconomic variables using a dataset that consists of 123 variables in quarterly frequency. These three approaches handle the dimensionality problem evoked by such a large dataset by aggregating...

This paper proposes a latent dynamic factor model for low- as well as high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and allows for flexible dynamic dependence patterns by combining common latent factors driven by HAR dynamics and...