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

  • Fabio Busetti

    ()

    (Bank of Italy, Economic Research Department)

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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File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2001/2001-0437/tema_437_01.pdf
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Paper provided by Bank of Italy, Economic Research and International Relations Area 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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  1. Stefano Siviero & Daniele Terlizzese, 2007. "Macroeconomic Forecasting: Debunking a Few Old Wives' Tales," Journal of Business Cycle Measurement and Analysis, OECD Publishing,Centre for International Research on Economic Tendency Surveys, vol. 2007(3), pages 287-316.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-19, June.
  8. Luigi Guiso & Luigi Pistaferri & Fabiano Schivardi, 2005. "Insurance within the Firm," Journal of Political Economy, University of Chicago Press, vol. 113(5), pages 1054-1087, October.
  9. 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.
  10. 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.
  11. repec:dgr:kubcen:1998141 is not listed on IDEAS
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