Survey Data as Coincident or Leading Indicators

By C. Frale, Massimiliano Marcellino, G. Mazzi, Tommaso Proietti. 2010

49 citations recorded by the source corpus.

In this paper we propose a monthly measure for the euro area Gross Domestic Product (GDP) based on a small scale factor model for mixed frequency data, featuring two factors: the first is driven by hard data, whereas the second captures the contribution of survey variables as coincident indicators. Within this framework we evaluate both the in-sample contribution of the second survey-based factor, and the short term forecasting performance of the model in a pseudo-real time experiment. We find that the survey-based factor plays a significant role for two components of GDP: Industrial Value Added and Exports. Moreover, the two factor model outperforms in terms of out of sample forecasting accuracy the traditional autoregressive distributed lags (ADL) specifications and the single factor model, with few exceptions for Exports and in growth rates.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/02ff8602fd7e4c246768505513130a024517c580

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In Economics and Econometrics

Citations in the corpus

A Survey of Econometric Methods for Mixed-Frequency Data (2013, methodology): “φp 02×2(5−p) ] ; F2 = [ I8 08×2 ] , (28)”
Introducing the Euro-STING: Short Term INdicator of Euro Area Growth (2010, methodology): “…over forecasting euro area GDP growth with large-scale factor models (Barhoumi et al., 2008, Angelini et al., 2008) versus small-to-medium-scale factor models such as ours and Frale et al. (2008), we agree with the view of Aruoba, Diebold and Scotti (2009) that the matter is ultimately empirical.”