Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK

Abstract

4Non-technical summary 5

The Role of Targeted Predictors for Nowcasting GDP with Bridge Models: Application to the Euro Area

“…Caggiano et al (2011) showed that using smaller subsets of the available large data set improves the forecast performance of factor models for the six largest Euro Area economies, the Euro Area aggregate and UK. Using Monte Carlo analyses, Alvarez et al (2012) show that small scale factor models outperform larger ones in terms of forecast precision.…”

The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time

“…In the literature, alternative techniques and methods have been introduced to perform this information reduction: targeting indicators with thresholding rules (Bai and Ng, 2008, Schumacher, 2010, Bulligan et al, 2012, and Kim and Swanson, 2013, estimating weighted principal components and preselecting indicators with rules that eliminate irrelevant information Ng, 2006, andCaggiano et al, 2011), estimating factors under a sparse prior (e.g. Kaufmann and Schumacher, 2013), and selecting one "representative" indicator of each category in which the large panel can be classified (Alvarez et al, 2012).…”

“…These options, together with the choice of the balancing strategy discussed above (shifting, autoregressive and other specialised algorithms) brings to alternative FM of which much of the literature has supported the usefulness in forecasting euro area GDP; see e.g. Marcellino et al (2003), Rünstler et al (2009), Angelini et al (2011). documented the advantage over simple benchmarks of forecasting euro area GDP with the PFM approach (see Caggiano et al, 2011;Bulligan et al, 2012 …”

Nowcasting and Forecasting Economic Growth in the Euro Area Using Principal Components

“…One of the issues is how to determine the optimal number of factors in the model; see, for example, Ng (2002, 2007). Another issue of debate is the determination of the optimal size of the database for the extraction of the factors; see, for example, the discussions in Caggiano et al (2011) andden Reijer (2013). A related issue that has attracted relatively little attention in the literature is the gain in forecast accuracy resulting from including autoregressive terms of the target variable in the model specification, i.e.…”