The paper addresses the question on what is the typical time horizon over which a full transmission of movements in the real exchange rate into real economy takes place. To this end, we base our analysis on the mixed-frequency small-scale dynamic factor model of Siliverstovs (2012) fitted to the...

We examine the short-term performance of two alternative approaches to forecasting using dynamic factor models. The first approach extracts the seasonal component of the individual indicators before estimating the dynamic factor model, while the alternative uses the non-seasonally adjusted data...

In this paper we propose a new real-time forecasting model for euro area GDP growth, D€STINY, which attempts to bridge the existing gap in the literature between large- and small-scale dynamic factor models. By adopting a disaggregated modelling approach, D€STINY uses most of the information...

GDP, the key statistics describing the state of the economy, is collected at low frequency, typically on a quarterly basis, and released with a substantial lag. The goal of this paper is to have the most timely and accurate idea about the current real economic activity, measured by the growth...

In this paper we develop a mixed frequency dynamic factor model featuring stochastic shifts in the volatility of both the latent common factor and the idiosyncratic components. We take a Bayesian perspective and derive a Gibbs sampler to obtain the posterior density of the model parameters. This...

Previous studies have shown that the effectiveness of monetary policy largely depends on market expectations about future policy actions. This paper proposes an econometric framework to address the effect of the current state of the economy on monetary policy expectations. Specifically, we study...

This paper derives forecasts for euro area real GDP growth based on a bottom up approach from the production side. That is, GDP is forecast via the forecasts of value added across the different branches of activity, which is quite new in the literature. Linear regression models in the form of...

In this report we document the ETLAnow project. ETLAnow is a model for forecasting with big data. At the moment, it predicts the unemployment rate in the EU-28 countries using Google search data. This document is subject to updates as the ETLAnow project advances.