We estimate and forecast growth in euro area monthly GDP and its components from a dynamic factor model due to Doz et al. (2005), which handles unbalanced data sets in an efficient way. We extend the model to integrate interpolation and forecasting together with cross-equation accounting...

This paper provides evidence on the reliability of euro area real-time output gap estimates. A genuine real-time data set for the euro area is used, including vintages of several sets of euro area output gap estimates available from 1999 to 2006. It turns out that real-time estimates of the...

This study examines empirically the information content of the euro area Bank Lending Survey for aggregate credit and output growth. The responses of the lending survey, especially those related to loans to enterprises, are a significant leading indicator for euro area bank credit and real GDP...

The paper provides a systematic empirical analysis of the role of the housing market in the macroeconomy in the US and in the euro area. First, it establishes some stylised facts concerning key variables in the housing market, such as the real house price, residential investment and mortgage...

In this paper we propose a methodology to estimate a dynamic factor model on data sets with an arbitrary pattern of missing data. We modify the Expectation Maximisation (EM) algorithm as proposed for a dynamic factor model by Watson and Engle (1983) to the case with general pattern of missing...

Any empirical analysis of the credit channel faces a key identification challenge: changes in credit supply and demand are difficult to disentangle. To address this issue, we use the detailed answers from the US and the confidential and unique Euro area bank lending surveys. Embedding this...

This paper is concerned with the estimation of euro area potential output growth and its decomposition according to the sources of growth. The growth accounting exercise is based on a multivariate structural time series model which combines the decomposition of total output according to the...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting in ation with the ability of quantile regression to...