Abstract
In this article, we develop a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time-varying stochastic volatilities. We use the model to investigate business cycle dynamics in the euro area and present three sets of empirical results. First, we evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how our setup allows to make a probabilistic assessment of the contribution of releases to forecast revisions. Third, we examine point and density out of sample forecast accuracy. We find that introducing stochastic volatility in the model contributes to an improvement in both point and density forecast accuracy. Supplementary materials for this article are available online.
Highlights
The conduct of monetary and fiscal policy relies on the timely assessment of current and future economic conditions.1 The task of providing an accurate picture of the current cyclical position is significantly plagued by the delay with which crucial economic indicators are released
In line with Clark (2011) we find that the introduction of stochastic volatility leads to an improvement of both point and density forecast accuracy
Our information set consists of nine indicators, namely our target variable, which is the rate of growth of quarterly GDP, two Industrial Production indicators, four surveys, the bilateral US dollar euro exchange rate and a the difference between the 3 months and the 10 years spread on US Government Bonds
Summary
The conduct of monetary and fiscal policy relies on the timely assessment of current and future economic conditions. The task of providing an accurate picture of the current cyclical position is significantly plagued by the delay with which crucial economic indicators are released. On the large data side, research by Angelini et al (2011) and Banbura and Modugno (2010) has documented the predictive content of a large number of indicators for GDP growth and introduced new tools to link monthly data releases to GDP forecast revisions. They can be detected with standard statistical tests and parameter instability can be either incorporated in the model or bypassed by splitting the sample or adopting a rolling estimation scheme These strategies, are not viable if breaks are, rather than large and discrete, small and continuous, a form of parameter time variation that has received a lot of attention in the macro empirical literature in the past decade.
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