We propose the adaptive elastic net for estimating Vector Autoregressions. Unlike competing methods, this estimator preserves the standard structural-VAR toolkit but at the same time leads to accurate forecasts. We show validity of the bootstrap in constructing unconditional-on-model-selection...

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...

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 compare real-time density forecasts for the euro area using three DSGE models. The benchmark is the Smets-Wouters model and its forecasts of real GDP growth and inflation are compared with those from two extensions. The first adds financial frictions and expands the observables to include a...

Commodity prices co-move, but the strength of this co-movement changes over time due to structural factors, like changing energy intensity in production and consumption as well as changing composition of underlying shocks. This paper explores whether econometric models that exploit this...

This paper addresses the poor performance of the Expectation-Maximization (EM) algorithm in the estimation of low-noise dynamic factor models, commonly used in macroeconomic forecasting and nowcasting. We show analytically and in Monte Carlo simulations how the EM algorithm stagnates in a...

The economic forecasts for Germany in the period 2001 to 2003 grossly missed reality. Forecasters estimated an average annual growth rate of 1.6 per cent, but real GDP actually grew by only 0.3 per cent per annum. In 2003 the real GDP in Germany even shrank by 0.1 per cent. Forecasters tend to...

We use a machine-learning approach known as Boosted Regression Trees (BRT) to reexamine the usefulness of selected leading indicators for predicting recessions. We estimate the BRT approach on German data and study the relative importance of the indicators and their marginal effects on the...

The paper investigates the sources of macroeconomic forecast errors in Germany. The predictions of the so-called "six leading" research institutes are analyzed. The forecast errors are discussed within an aggregate demand/supply scheme. Structural Vector Autoregressive Models are estimated to...

The paper investigates the sources of macroeconomic forecast errors in Germany. The predictions of the so-called "six leading" research institutes are analyzed. The forecast errors are discussed within an aggregate demand/supply scheme. Structural Vector Autoregressive Models are estimated to...