This paper discusses how the forecast accuracy of a Bayesian vector autoregression(BVAR) is affected by introducing the zero lower bound on the federal funds rate. As abenchmark I adopt a common BVAR specification, including 18 variables, estimatedshrinkage, and no nonlinearity. Then I entertain...

In this paper, we ask whether it is possible to forecast gross value-added (GVA) and its sectoral subcomponents at the regional level. With an autoregressive distributed lagmodel we forecast total and sectoral GVA for one German state (Saxony) with more than 300 indicators from different...

Forecast models with large cross-sections are often subject to overparameterization leading to unstable parameter estimates and hence inaccurate forecasts. Recent articlessuggest that a large Bayesian vector autoregression (BVAR) with sufficient prior information dominates competing approaches....

It can be shown that inflation expectations and associated forecast errors are characterized by a high degree of persistence. One reason may be that forecasters cannot directly observe the inflation target pursued by the central bank and, hence, face a complicated forecasting problem. In...

Given the relatively low computational effort involved, vector autoregressive (VAR)models are frequently used for macroeconomic forecasting purposes. However, the usuallylimited number of observations obliges the researcher to focus on a relatively smallset of key variables, possibly discarding...

Any measure of unobserved inflation uncertainty relies on specific assumptions which are most likely not fulfilled completely. This calls into question whether an individual measure delivers a reliable signal. To reduce idiosyncratic measurement error, we propose using common information...

Different studies provide surprisingly a large variety of controversial conclusions aboutthe forecasting power of an indicator, even when it is supposed to forecast the sametime series. In this study we aim to provide a thorough overview of linear forecastingtechniques and draw conclusions...

This paper documents the presence of systematic bias in the real GDP and inflation forecasts of private sector forecasters in the G7 economies in the years 1990-2005. The data come from the monthly Consensus Economics forecasting service, and bias is measured and tested for significance using...

Using a standard decomposition of forecasts errors into common and idiosyncratic shocks, we show that aggregate forecast uncertainty can be expressed as the disagreement among the forecasters plus the perceived variability of future aggregate shocks. Thus, the reliability of disagreement as a...

Can information on macroeconomic uncertainty improve the forecast accuracy for key macroeconomic time series for the US? Since previous studies have demonstrated that the link between the real economy and uncertainty is subject to nonlinearities, I assess the predictive power of macroeconomic...