Predictions vs. Preliminary Sample Estimates: The Case of Eurozone Quarterly GDP

Open access


Economic agents are aware of incurring a loss in basing their decisions on their own extrapolations instead of on sound statistical data, but this loss may be smaller than the one related to waiting for the dissemination of the final data. Broad guidelines on deciding when statistical offices should release preliminary and final estimates of the key statistics may come from comparing the loss attached to users’ predictions with the loss associated to possible preliminary estimates from incomplete samples. Furthermore, the cost of delaying decisions may support the dissemination of very early estimates of economic indicators, even if their accuracy is not fully satisfactory from a strict statistical viewpoint. Analysing the vintages of releases of quarterly Euro area GDP supports the view that even very inefficient predictions may beat some official preliminary releases of GDP, suggesting that the current calendar of data dissemination requires some adjustment. In particular, actual “flash” estimates could be anticipated, while some later intermediate releases are likely less informative for the users.

Altavilla, C. and M. Ciccarelli. 2007. “Information Combination and Forecast (st)ability.

Evidence from Vintages of Time-Series Data.” Working Paper Series ECB, No. 864. Available at: (accessed July 31, 2014).

Angelini, E., G. Camba-Mendez, D. Giannone, L. Reichlin, and G. Ru¨nstler. 2011. “Short- Term Forecasts of Euro Area GDP Growth.” The Econometrics Journal 14: 25-44. DOI:

Barhoumi, K., S. Benk, R. Cristadoro, A. Reijer, P. Jakaitiene, P. Jelonek, and A. Rua. 2008. “Short-Term Forecasting of GDP Using Large Monthly Datasets: a Pseudo Real-Time Forecast Evaluation Exercise.” Occasional Paper Series ECB, No. 84. Available at: (accessed July 31, 2014).

Blanchard, O.J., J.P. L’Huillier, and G. Lorenzoni. 2009. “News, Noise, and Fluctuations: An Empirical Exploration.” NBER Working Paper, No. w15015, Available at: http:// (accessed July 31, 2014).

Clemen, R. 1989. “Combining Forecasts: a Review and Annotated Bibliography.” International Journal of Forecasting 5: 559-583. DOI:

D’Orazio, M., M. Di Zio, and M. Scanu. 2006. Statistical Matching: Theory and Practice. New York: Wiley.

Diron, M. 2008. “Short-Term Forecasts of Euro Area Real GDP Growth: An Assessment of Real-Time Performance Based on Vintage Data.” Journal of Forecasting 27: 371-390. DOI:

European Central Bank 2009. “Revisions to GDP Estimates in the Euro Area.” Monthly Bulletin 4: 85-90. Available at: (accessed July 31, 2014).

European Communities 1999. Handbook on Quarterly National Accounts, Luxembourg: Office for Official Publications of the European Communities. Available at: (accessed July 31, 2014).

European Statistics Code of Practice 2011. Available at:

Fan, J. 1992. “Design-Adaptive Nonparametric Regression.” Journal of the American Statistical Association 87: 998-1004. DOI:

Fan, J. and I. Gijbels. 1996. Local Polynomial Modelling and Its Applications. London: Chapman & Hall.

Fixler, D.J. and B.T. Grimm. 2006. “GDP Estimates: Rationality Tests and Turning Point Performance.” Journal of Productivity Analysis 25: 213-229. DOI:

Frale, C. and V. Raponi. 2012. “Revisions in Official Data and Forecasting.” Working Papers of Dipartimento del Tesoro, No. 3.

Frale, C., M. Marcellino, G.L. Mazzi, and T. Proietti. 2011. “EUROMIND: A Monthly Indicator of the Euro Area Economic Conditions.” Journal of the Royal Statistical Society: Series A 174: 439-470. DOI:

Giannone, D., L. Reichlin, and D. Small. 2008. “Nowcasting: The Real-Time Informational Content of Macroeconomic Data.” Journal of Monetary Economics 55: 665-676. DOI:

Graham, P., J. Young, and R. Penny. 2009. “Multiply Imputed Synthetic Data: Evaluation of Hierarchical Bayesian Imputation Models.” Journal of Official Statistics 25: 245-268.

Granger, C.W.J. and M.J. Machina. 2006. “Forecasting and Decision Theory.” In Handbook of Economic Forecasting, edited by G. Elliott, C.W.J. Granger, and A. Timmermann. Amsterdam: Elsevier.

Granger, C.W.J. and M.H. Pesaran. 2000. “Economic and Statistical Measures of Forecast Accuracy.” Journal of Forecasting 19: 537-560. DOI:,537:AID-FOR769.3.0.CO;2-G.

Jansen, W.J., X. Jin, and J. de Winter. 2012. “Forecasting and Nowcasting Real GDP: Comparing Statistical Models and Subjective Forecasts.” De Nederlandsche Bank Working Paper, No. 365. Available at: (accessed July 31, 2014).

Little, R.J.A. 2012. “Calibrated Bayes, an Alternative Inferential Paradigm for Official Statistics.” Journal of Official Statistics 28: 309-334.

Little, R.J.A., F. Liu, and T.E. Raghunathan. 2004. “Statistical Disclosure Techniques Based on Multiple Imputation.” In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, edited by A. Gelman and X.L. Meng, 141-152. New York: John Wiley & Sons.

Pain, N. and F. Se´dillot. 2005. “Indicator Models of Real GDP Growth in the Major OECD Economies.” OECD Economic Studies, No. 40. Available at:¼4a74b653-6721-45c4-897d-9aa24c0c2037%40sessionmgr113&vid¼2&hid¼128. (accessed July 31, 2014).

Sarndal, C.-E. and S. Lundstro¨m. 2005. Estimation in Surveys With Nonresponse. New York: John Wiley & Sons.

Sims, C.A. 2003. “Implications of Rational Inattention.” Journal of Monetary Economics 50: 665-690. DOI:

UNSTAT, 2009. “International Seminar on Timeliness, Methodology and Comparability of Rapid Estimates of Economic Trends.” Available at: (accessed July 31, 2014).

Winston, G.C. 2008. The Timing of Economic Activities. Cambridge: Cambridge University Press.

Yang, Y. and H. Zou. 2004. “Combining Time Series Models for Forecasting.” International Journal of Forecasting 20: 69-84. DOI:

Journal of Official Statistics

The Journal of Statistics Sweden

Journal Information

IMPACT FACTOR 2017: 0.662
5-year IMPACT FACTOR: 1.113

CiteScore 2017: 0.74

SCImago Journal Rank (SJR) 2017: 1.158
Source Normalized Impact per Paper (SNIP) 2017: 0.860


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 787 787 295
PDF Downloads 865 865 341