No abstract

The simplicity of the standard diffusion index model of Stock and Watson has certainly contributed to its success among practitioners, resulting in a growing body of literature on factor-augmented forecasts. However, as pointed out by Bai and Ng, the ranked factors considered in the forecasting equation depend neither on the variable to be forecast nor on the forecasting horizon. We propose a refinement of the standard approach that retains the computational simplicity while coping with this limitation. Our approach consists of generating a weighted average of all the principal components, the weights depending both on the eigenvalues of the sample correlation matrix and on the covariance between the estimated factor and the targeted variable at the relevant horizon. This 'targeted diffusion index' approach is applied to US data and the results show that it outperforms considerably the standard approach in forecasting several major macroeconomic series. Moreover, the improvement is more significant in the final part of the forecasting evaluation period. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we propose a quantitative measure for inflation expectations based on consumer survey data. Thereafter, we proceed to testing the rationality assumption. This issue is of noteworthy interest in its own as it is commonly assumed in the theoretical modelling literature that the rational expectations hypothesis holds. This analysis is conducted for the euro area as a whole, as well as for several member countries, using a sample covering the last two decades. Moreover, we also assess if the conclusions hold when one focuses on the post-euro introduction period.

Inflation perceptions, Probability method, Balance statistic, Euro area cash changeover, Cointegration breakdown, C16, C42, E31,

As macroeconomic data are released with different delays, one has to handle unbalanced panel data sets with missing values at the end of the sample period when estimating dynamic factor models. We propose an EM algorithm which copes with such data sets while accounting for autoregressive common factors and allowing for serial correlation in the idiosyncratic components. Based on Monte Carlo simulations, we find that taking on board the dynamics of the idiosyncratic components improves significantly the accuracy of the estimation of both the missing values and the common factors at the end of the sample period.