Survey Data as Coincident or Leading Indicators

Cecilia Frale (), Massimiliano Marcellino, Gian Luigi Mazzi () and Tommaso Proietti

No 3, Working Papers from Department of the Treasury, Ministry of the Economy and of Finance

Abstract: 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.

Keywords: Survey data; Forecasting; Temporal Disaggregation; Dynamic factor modes; Kalman Filter and smoother (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 (search for similar items in EconPapers)
Pages: 33
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Survey data as coincident or leading indicators (2010)
Working Paper: Survey Data as Coicident or Leading Indicators (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:itt:wpaper:wp2009-3

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