Preliminary Data and Econometric Forecasting: An Application with the Bank of Italy Quarterly Model
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 naïve 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 multistep-ahead naïve 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 numerous papers. Here we consider two simple methods to combine, in general sub-optimally, 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. 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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
|Date of creation:||May 2004|
|Contact details of provider:|| Postal: Centre for Economic Policy Research, 77 Bastwick Street, London EC1V 3PZ.|
Phone: 44 - 20 - 7183 8801
Fax: 44 - 20 - 7183 8820
|Order Information:|| Email: |
References listed on IDEAS
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.:
- 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.
- A. C. Harvey & Siem Jan Koopman, 2000. "Computing Observation Weights for Signal Extraction and Filtering," Econometric Society World Congress 2000 Contributed Papers 0888, Econometric Society.
- 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.
- Yates, Tony & Richard Harrison & George Kapetanios, 2003.
"Forecasting with measurement errors in dynamic models,"
Royal Economic Society Annual Conference 2003
225, Royal Economic Society.
- Harrison, Richard & Kapetanios, George & Yates, Tony, 2005. "Forecasting with measurement errors in dynamic models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 595-607.
- Richard Harrison & George Kapetanios & Tony Yates, 2004. "Forecasting with measurement errors in dynamic models," Bank of England working papers 237, Bank of England.
- Richard Harrison & George Kapetanios & Tony Yates, 2004. "Forecasting with Measurement Errors in Dynamic Models," Working Papers 521, Queen Mary University of London, School of Economics and Finance.
- Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
- Bordignon, Silvano & Trivellato, Ugo, 1989. "The Optimal Use of Provisional Data in Forecasting with Dynamic Model s," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(2), pages 275-286, April.
- Durbin, James & Koopman, Siem Jan, 2012.
"Time Series Analysis by State Space Methods,"
Oxford University Press,
edition 2, number 9780199641178.
- Rosanne Cole, 1969. "Errors in Provisional Estimates of Gross National Product," NBER Books, National Bureau of Economic Research, Inc, number cole69-1, September.
- 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 386-393, August.
- 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 445-453, October.
- 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.).
- Rosanne Cole, 1969. "Introduction to "Errors in Provisional Estimates of Gross National Product"," NBER Chapters, in: Errors in Provisional Estimates of Gross National Product, pages 3-6 National Bureau of Economic Research, Inc.
- 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.
- Patterson, K. D., 1995. "Forecasting the final vintage of real personal disposable income: A state space approach," International Journal of Forecasting, Elsevier, vol. 11(3), pages 395-405, September.
- 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.
When requesting a correction, please mention this item's handle: RePEc:cpr:ceprdp:4382. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.