Nowadays a considerable amount of information on the behavior of the economy is readily available, in the form of large datasets of macroeconomic variables. Central bankers can be expected to base their decisions on this very large information set, so that it can be difficult to track their...

The recent crisis has emphasized the role of financial - macroeconomic interactions, and international trade in goods and services, in the transmission of the shocks. Both phenomena, closely related to the higher degree of globalization, are very relevant for small open economies, and...

The accession of ten countries into the European Union makes the forecasting of their key macroeconomic indicators such as GDP growth, inflation and interest rates an exercise of some importance. Because of the transition period, only short spans of reliable time series are available which...

This paper brings together several important strands of the econometrics literature: errorcorrection, cointegration and dynamic factor models. It introduces the Factor-augmented Error Correction Model (FECM), where the factors estimated from a large set of variables in levels are jointly...

In this paper we evaluate the role of a set of variables as leading indicators for Euro-area inflation and GDP growth. Our evaluation is based on using the variables in the ECB Euroarea model database, plus a set of similar variables for the U.S. We compare the forecasting performance of each...

In this paper we analyze a novel dataset of Business and Consumer Surveys, using dynamic factor techniques, to produce composite coincident indices (CCIs) at the sectoral level for the European countries and for Europe as a whole. Few CCIs are available for Europe compared to the US, and most of...

Pooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improves the quality of the forecast. In this paper we evaluate whether pooling interpolated or backdated time series obtained from different procedures can also improve the...

In this paper we evaluate the relative merits of three approaches to information extraction from a large data set for forecasting, namely, the use of an automated model selection procedure, the adoption of a factor model, and single-indicator-based forecast pooling. The comparison is conducted...

We conduct a detailed simulation study of the forecasting performance of diffusion index-based methods in short samples with structural change. We consider several data generation processes, to mimic different types of structural change, and compare the relative forecasting performance of factor...

“Iterated” multiperiod ahead time series forecasts are made using a one-period ahead model, iterated forward for the desired number of periods, whereas “direct” forecasts are made using a horizon-specific estimated model, where the dependent variable is the multi-period ahead value being...