Multivariate unobserved components (structural) time series models are fitted to annual post-war observations on real income per capita in countries in the euro zone. The aim is to establish stylized facts about convergence as it relates both to long-run income levels and to cycles. The analysis...

This paper analyzes the pass-through from import prices to CPI inflation in real time. Our strategy follows an event-study approach, which compares inflation forecasts before and after import price releases. Inflation forecasts are modelled using a dynamic factor procedure that relies on daily...

A new procedure for shock identification of macroeconomic forecasts based on factor analysis is proposed. The identification scheme relies on daily panels and on the recognition that macroeconomic releases exhibit a high level of clustering. A large number of data releases on a single day is of...

The timely release of macroeconomic data imposes a distinct structure on the panel: the clustering and sequential ordering of real and nominal variables. We call this orderly release of economic data sequential information flow. The ordered panel generates a new class of restrictions that are...

Using a stochastic frontier model and a comprehensive dataset, we study factors that affect corporate efficiency in Europe. We find that (i) larger firms are less efficient than smaller firms, (ii) greater leverage contributes to corporate efficiency, and (iii) high competition is less...

In February 2008, British Telecommunications (BT) introduced automatically renewing, or ‘rollover’, contracts into the UK market for fixed-voice telephone service. These contracts included a 12-month Minimum Contract Period (MCP) with associated Early Termination Charges (ETCs). Unless...

In this paper, we construct a large Bayesian Vector Autoregressive model (BVAR) for the Euro Area that captures the complex dynamic inter-relationships between the main components of the Harmonized Index of Consumer Price (HICP) and their determinants. The model is estimated using Bayesian...

We define nowcasting as the prediction of the present, the very near future and the very recent past. Key in this process is to use timely monthly information in order to nowcast quarterly variables that are published with long delays. We argue that the nowcasting process goes beyond the simple...

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance for US time series with the most promising existing alternatives, namely, factor...

This paper shows consistency of a two step estimator of the parameters of a dynamic approximate factor model when the panel of time series is large (n large). In the first step, the parameters are first estimated from an OLS on principal components. In the second step, the factors are estimated...