This paper considers the problem of forecasting real and financial macroeconomic variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model previously estimated over the 1979Q1-2003Q4 period by Dees, de Mauro, Pesaran, and Smith...

The popular scholarly exercise of evaluating exchange rate forecasting models relative to a random walk was stimulated by the well-cited Meese and Rogoff (1983) paper. Practitioners who construct quantitative models for trading exchange rates approach forecasting from a different perspective....

In this paper, we examine the role of global and domestic credit supply shocks in macroeconomic fluctuations for Emerging Markets. For this purpose, we impose a set of zero and sign restrictions within a medium-scale Bayesian Vector Auto-Regressive model. Quarterly data from South Africa and G-7...

This paper proposes a new method of forecasting euro area quarterly real GDP that uses area-wide indicators, which are derived by optimally pooling the information contained in national indicator series. Following the ideas of predictive modeling, we construct the area-wide indicators by...

We apply non-linear error-correction models to the empirical testing of the sustainability of the government’s intertemporal budget constraint. Our empirical analysis, based on Italy, shows that the Italian government is meeting its intertemporal budget constraint, in spite of the high levels...

The ‘saving for a rainy day’ hypothesis implies that households’ saving decisions reflect that they can (rationally) predict future income declines. The empirical relevance of this hypothesis plays a key role in discussions of fiscal policy multipliers and it holds under the null that the...

We investigate the role of crude oil spot and futures prices in the process of price discovery by using a cost-of-carry model with an endogenous convenience yield and daily data over the period from January 1990 to December 2008. We provide evidence that futures markets play a more important...

This paper provides a general strategy for analyzing monetary policy in real time which accounts for data uncertainty without explicitly modelling the revision process. The strategy makes use of all the data available from a real-time data matrix and averages model estimates across all data...

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...

In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a unique data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden-Württemberg) and Eastern Germany. We overcome the problem of a...