D'Elia, Enrico (2010): Predictions vs preliminary sample estimates.
Download (309kB) | Preview
In general, rational economic agents are not in the position to wait for the statistical agencies disseminate the final results of the relevant surveys before making a decision, and have to make use of some model based predictions, even when agents are not assumedly forward looking. Thus, from the viewpoint of agents, predictions and preliminary results from surveys often compete against each other. Agents are aware to incur in a loss basing their decisions on predictions instead of sound statistical data, but the loss could be smaller than the one related to waiting for the dissemination of final data. Comparing the loss attached to predictions, on the one hand, and to possible preliminary estimate from incomplete samples, on the other, provides a broad guidance in deciding if and when statistical agencies should release preliminary and final estimates of the key variables. The main result of the analysis is that, in general, preliminary sample estimates are useful for the users only if they come from unexpectedly large sub-samples, even when the predictability of relevant variables is scarce. Nevertheless, the cost of delaying decisions for many economic agents may support the dissemination of early estimates of the main economic aggregates even if their accuracy is not fully satisfactory from a strict statistical viewpoint.
|Item Type:||MPRA Paper|
|Original Title:||Predictions vs preliminary sample estimates|
|Keywords:||Accuracy; Data Dissemination; Forecast; Preliminary Estimates; Timeliness|
|Subjects:||C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory
C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C82 - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data ; Data Access
|Depositing User:||Enrico D'Elia|
|Date Deposited:||20 Jan 2012 02:18|
|Last Modified:||08 Oct 2016 10:17|
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.
Altissimo, F. et al. (2007), “New EUROCOIN: tracking economic growth in real time”, Banca d'Italia, Temi di discussione, No. 631.
Angelini, E., G. Camba-Mendez, D. Giannone, L. Reichlin, and G. Rünstler (2008), “Short-term Forecasts of Euro Area GDP Growth”, ECB Working Paper, No. 949.
Barhoumi, K. et al. (2008), “Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise”, Occasional Paper Series ECB, No. 84.
Blanchard, O. J., J. P. L'Huillier and G. Lorenzoni (2009), “News, noise, and fluctuations: An empirical exploration”, NBER Working Paper, No. w15015
Bruneau, C., O. de Bandt, A. Flageollet and E. Michaux (2007), “Forecasting inflation using economic indicators: the case of France”, Journal of Forecasting, Vol. 26, No. 1, pp. 1-22.
Clemen, R. (1989), “Combining forecasts: a review and annotated bibliography”, International Journal of Forecasting, Vol. 5, pp. 559-583.
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, Vol. 27, pp. 371-390.
Duarte, C. and A. Rua (2007), Forecasting infl ation through a bottom-up approach: How bottom is bottom? Economic Modelling, Vol. 24, pp. 941-953.
European Central Bank (2009), “Revisions to GDP estimates in the euro area”, Monthly Bulletin, No. 4, pp. 85-90.
Giannone D., L. Reichlin, D. Small (2008), “Nowcasting: The real-time informational content of macroeconomic data”, Journal of Monetary Economics, Vol. 55, No. 4, pp. 665-676
Granger, C.W.J. and M. J. Machina (2006), “Forecasting and decision theory”, in Elliott, G., C.W.J. Granger and A. Timmermann (eds.), Handbook of Economic Forecasting, Vol. 1, Elsevier.
Granger, C.W.J. and M.H. Pesaran (2000), “Economic and statistical measures of forecast accuracy”. Journal of Forecasting, Vol. 19, pp. 537–560.
Pain, N. and F. Sédillot (2005), “Indicator models of real GDP growth in the major OECD economies”, OECD Economic Studies, No. 40.
Sarndal, C. E. (2005), Estimation in surveys with nonresponse, Lundstrom, John Wiley & Sons.
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. Inter-national Journal of Forecasting, vol. 20, pp.69-84.