EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area

Abstract

EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom-up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and ragged-edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process sequentially the data as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules.

Nowcasting Monthly GDP with Big Data: A Model Averaging Approach

“…Another important extension deals with density forecasting with many potential indicators, following Aastveit et al (2014) and Proietti et al (2017). In particular, the nowcasting and forecasting densities arising from the bivariate models could be aggregated according to approach introduced by Gneiting and Ranjan (2013).…”

“…Euro-Sting, see Camacho and Pérez-Quirós (2010), is a monthly indicator of the euro area Gross Domestic Product (GDP), based on a parametric dynamic factor model with mixed frequency data, constructed as an extension of the model described in Mariano and Murasawa (2003). Density forecasting and nowcasting has been considered by Aastveit et al (2014), Mazzi et al (2014) and Proietti et al (2017).…”

“…PROIETTI and GIOVannELLI (2003). Density forecasting and nowcasting has been considered by Aastveit et al (2014), Mazzi et al (2014) and Proietti et al (2017). Bańbura and Rünstler (2011) deal with forecasting the quarterly euro area GDP growth using a large-dimensional mixed-frequency dynamic factor model; they assess the contribution of the individual monthly indicators in forming the GDP growth forecast by looking at the forecast weights and document the relevance of business and consumer survey data for nowcasting GDP.…”

Nowcasting Monthly GDP with Big Data: A Model Averaging Approach

“…Another important extension deals with density forecasting with many potential indicators, following Aastveit et al (2014) and Proietti et al (2017). In particular, the nowcasting and forecasting densities arising from the bivariate models could be aggregated according to approach introduced by Gneiting and Ranjan (2013).…”

“…Euro-Sting, see Camacho and Pérez-Quirós (2010), is a monthly indicator of the euro area Gross Domestic Product (GDP), based on a parametric dynamic factor model with mixed frequency data, constructed as an extension of the model described in Mariano and Murasawa (2003). Density forecasting and nowcasting has been considered by Aastveit et al (2014), Mazzi et al (2014) and Proietti et al (2017).…”