Introduction

The accession of ten countries to the European Union makes the forecasting of their key macroeconomic indicators such as GDP growth, inflation and interest rates an exercise of obvious importance. Because of the transition period, only short spans (denoted T) of reliable time series are available for each of these countries. This suggests the adoption of simple time series models as forecasting tools, because of their parsimonious specification and good performance (based on results available from studies for other countries).

However, despite the constraints on the time span of data, a large number of macroeconomic series of potential use in forecasting (for a given time span) are available for each country. This makes the recently proposed dynamic factor models a viable and alternative forecasting tool, where the limitations on estimation and forecasting implied by the short length of time series are compensated by extending the longitudinal dimensional (denoted N) of the data.

Dynamic factor models have been successfully applied in a number of papers to forecasting macroeconomic variables for the US and Euro area, including Stock and Watson (1999, 2002a, 2002b) and Marcellino, Stock and Watson (2001, 2003). Earlier applications of factor models include Geweke (1977), Sargent and Sims (1977), Engle and Watson (1981) and Stock and Watson (1991) who estimated small-N dynamic factor models in the time domain, where N denotes the number of variables in the data set on which information is available.