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Forecasting with measurement errors in dynamic models

  • Yates, Tony

    (Bank of England)

  • Richard Harrison
  • George Kapetanios

This paper explores the effects of measurement error on dynamic forecasting models. The paper sets out to illustrate a trade off that confronts forecasters and policymakers when they use data that are measured with error. On the one hand, observations on recent data give valuable clues as to the shocks that are hitting the system and will be propagated into the variables to be forecast (and which ultimately will inform monetary policy). But on the other, those recent observations are likely to be those least well measured. Two broad classes of results are illustrated. The first relates to cases where it is imagined that the forecaster takes the coefficients in the data generating process as a given, and has to choose how much of the historical time series of data to use to form a forecast. It is shown that if recent data is sufficiently badly measured, relative to older data, that it can be optimal in this case not to use old data at all. The second class of results is more general. Here, it is shown that for a general class of linear autoregressive forecasting models, the optimal weight to place on a data observation of some age, relative to the weight in the true data generating process, will depend on the measurement error in that data. The gains to be had in forecasting are illustrated using a model of UK business investment growth.

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Paper provided by Royal Economic Society in its series Royal Economic Society Annual Conference 2003 with number 225.

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Date of creation: 04 Jun 2003
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Handle: RePEc:ecj:ac2003:225
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  2. Fabio Busetti, 2006. "Preliminary data and econometric forecasting: an application with the Bank of Italy Quarterly Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 1-23.
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  16. Athanasios Orphanides and Simon van Norden, 2001. "The Reliability of Inflation Forecasts Based on Output Gaps in Real Time," Computing in Economics and Finance 2001 247, Society for Computational Economics.
  17. 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.).
  18. Hoffman, Dennis L & Rasche, Robert H, 1996. "Assessing Forecast Performance in a Cointegrated System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 495-517, Sept.-Oct.
  19. Fabio Busetti, 2001. "The use of preliminary data in econometric forecasting: an application with the Bank of Italy Quarterly Model," Temi di discussione (Economic working papers) 437, Bank of Italy, Economic Research and International Relations Area.
  20. Patterson, Kerry D & Heravi, Saeed M, 1991. "Data Revisions and the Expenditure Components of GDP," Economic Journal, Royal Economic Society, vol. 101(407), pages 887-901, July.
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