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The use of preliminary data in econometric forecasting: an application with the Bank of Italy Quarterly Model

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Author Info
Fabio Busetti () (Bank of Italy, Economic Research Department)

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Abstract

This paper considers forecasting by econometric and time series models using preliminary (or provisional) data. The standard practice is to ignore the distinction between provisional and final data. We call the forecasts that ignore such a distinction naive forecasts, which are generated as projections from a correctly specified model using the most recent estimates of the unobserved final figures. It is first shown that in dynamic models a multistepahead naive forecast can achieve a lower mean square error than a single-step-ahead one, intuitively because it is less affected by the measurement noise embedded in the preliminary observations. The best forecasts are obtained by combining, in an optimal way, the information provided by the model with the new information contained in the preliminary data. This can be done in the state space framework, as suggested in the literature. Here we consider two simple methods to combine, in general suboptimally, the two sources of information: modifying the forecast initial conditions via standard regressions and using intercept corrections. The issues are explored with reference to the Italian national accounts data and the Bank of Italy Quarterly Econometric Model (BIQM). A series of simulation experiments with the model show that these methods are quite effective in reducing the extra volatility of prediction due to the use of preliminary data.

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Paper provided by Bank of Italy, Economic Research Department in its series Temi di discussione (Economic working papers) with number 437.

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Date of creation: Dec 2001
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Handle: RePEc:bdi:wptemi:td_437_01

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Web page: http://www.bancaditalia.it
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Related research
Keywords: preliminary data; macroeconomic forecasting; Bank of Italy Quarterly Econometric Model;

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Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

References listed on IDEAS
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  1. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May. [Downloadable!] (restricted)
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  2. Stefano Siviero & Daniele Terlizzese, 2001. "Macroeconomic forecasting: Debunking a few old wives' tales," Temi di discussione (Economic working papers) 395, Bank of Italy, Economic Research Department. [Downloadable!]
  3. Howrey, E Philip, 1984. "Data Revision, Reconstruction, and Prediction: An Application to Inventory Investment," The Review of Economics and Statistics, MIT Press, vol. 66(3), pages 386-93, August. [Downloadable!] (restricted)
  4. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  5. Patterson, K. D., 2000. "Which vintage of data to use when there are multiple vintages of data?: Cointegration, weak exogeneity and common factors," Economics Letters, Elsevier, vol. 69(2), pages 115-121, November. [Downloadable!] (restricted)
  6. Trivellato, Ugo & Rettore, Enrico, 1986. "Preliminary Data Errors and Their Impact on the Forecast Error of Simultaneous-Equations Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 445-53, October.
  7. Jon Faust & John H. Rogers & Jonathan H. Wright, 2000. "News and noise in G-7 GDP announcements," International Finance Discussion Papers 690, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  8. Howrey, E Philip, 1978. "The Use of Preliminary Data in Econometric Forecasting," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 193-200, May. [Downloadable!] (restricted)
  9. Luigi Guiso & Luigi Pistaferri & Fabiano Schivardi, 2002. "Insurance within the firm," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C3-1, International Conferences on Panel Data. [Downloadable!]
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  10. Koopman, S.J. & Shephard, N. & Doornik, J.A., 1998. "Statistical algorithms for models in state space using ssfpack 2.2," Discussion Paper 141, Tilburg University, Center for Economic Research. [Downloadable!]
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  11. Giampiero M. Gallo & Massimiliano Marcellino, . "Ex Post and Ex Ante Analysis of Provisional Data," Working Papers 141, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Lupi, Claudio & Peracchi, Franco, 2003. "The limits of statistical information: How important are GDP revisions in Italy?," Economics & Statistics Discussion Papers esdp03005, University of Molise, Dept. SEGeS. [Downloadable!]
  2. Richard Harrison & George Kapetanios & Tony Yates, . "Forecasting with measurement errors in dynamic models," Bank of England working papers 237, Bank of England. [Downloadable!]
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  3. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research Department. [Downloadable!]
  4. Giancarlo Corsetti & Luca Dedola, 2002. "Macroeconomics of international price discrimination," Temi di discussione (Economic working papers) 461, Bank of Italy, Economic Research Department. [Downloadable!]
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