Short-Term Forecasts of Euro Area Real GDP Growth: An Assessment of Real-Time Performance Based on Vintage Data

By Marie Diron. Social Science Research Network, 2008

210 citations recorded by the source corpus.

Forecasters commonly predict real gross domestic product growth from monthly indicators such as industrial production, retail sales and surveys, and therefore require an assessment of the reliability of such tools. While forecast errors related to model specification and unavailability of data in real time have been assessed, the impact of data revisions on forecast accuracy has seldom been evaluated, especially for the euro area. This paper proposes to evaluate the contributions of these three sources of forecast error using a set of data vintages for the euro area. The results show that gains in accuracy of forecasts achieved by using monthly data on actual activity rather than surveys or financial indicators are offset by the fact that the former set of monthly data is harder to forecast and less timely than the latter set. These results provide a benchmark which future research may build on as more vintage datasets become available. Copyright © 2008 John Wiley & Sons, Ltd.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/c862637174221a732f5a9ae7640727d310879347

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Citations in the corpus

Introducing the Euro-STING: Short Term INdicator of Euro Area Growth (2010, methodology): “As in these proposals, we diverge from the euro area univariate bridge equations employed by Runstler and Sedillot (2003) and Diron (2006) and from those which try to measure high-frequency objects (as real-time activity) on a daily or hourly basis, such as Aruoba, Diebold and Scotti (2009).”
Now-Casting and the Real-time Data Flow (2013, unknown): “In case the information set is large, forecast combination is often performed (Kitchen and Monaco, 2003; Diron, 2008; Angelini, CambaMéndez, Giannone, Reichlin, and Rünstler, 2011; Rünstler, Barhoumi, Benk, Cristadoro, Reijer, Jakaitiene, Jelonek, Rua, Ruth, and Nieuwenhuyze, 2009). Bridge equations”
Early Estimates of Euro Area Real GDP Growth: A Bottom Up Approach from the Production Side (2008, methodology): “This is documented in a revision analysis by Diron (2008).”
A Survey of Econometric Methods for Mixed-Frequency Data (2013, methodology): “One of the early econometric approaches in the presence of mixed-frequency data relies on the use of bridge equations, see e.g. Baffi gi, Golinelli, Parigi (2004) and Diron (2008).”
Estimating and Forecasting the Euro Area Monthly National Accounts from a Dynamic Factor Model (2010, unknown): “…from the j VARs, byQi;t+h = n 1 nX j=1 byQ;(j)i;t+h (12) - Bridge equations are widely used for the short-term forecasting of GDP and its components (e.g., Ba¢ gi et al., 2004; Rünstler and Sédillot, 2003; Diron, 2006), as they employ intraquarter information from the individual indicators.”
A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP (2007, unknown): “However, the findings of Diron (2006) suggest that this effect is very limited.”