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Forecasting with measurement errors in dynamic models

  • Richard Harrison
  • George Kapetanios
  • Tony Yates

This paper explores the effects of measurement error on dynamic forecasting models. It illustrates 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 that will be propagated into the variables to be forecast. But on the other, those recent observations are likely to be those least well measured. The paper studies two classes of forecasting problem. The first class includes cases where the forecaster takes the coefficients in the data-generating process as given, and has to choose how much of the historical time series of data to use to form a forecast. We show that if recent data are sufficiently badly measured, relative to older data, it can be optimal not to use recent data at all. The second class of problems we study is more general. We show 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 observation. We illustrate the gains in forecasting performance using a model of UK business investment growth.

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Paper provided by Bank of England in its series Bank of England working papers with number 237.

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Date of creation: Nov 2004
Date of revision:
Handle: RePEc:boe:boeewp:237
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  1. David Hendry & Michael P. Clements, 2001. "Economic Forecasting: Some Lessons from Recent Research," Economics Papers 2002-W11, Economics Group, Nuffield College, University of Oxford.
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  9. Egginton, Don M. & Pick, Andreas & Vahey, Shaun P., 2002. "'Keep it real!': a real-time UK macro data set," Economics Letters, Elsevier, vol. 77(1), pages 15-20, September.
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  11. Athanasios Orphanides & Simon van Norden, 1999. "The reliability of output gap estimates in real time," Finance and Economics Discussion Series 1999-38, Board of Governors of the Federal Reserve System (U.S.).
  12. Sargent, Thomas J, 1989. "Two Models of Measurements and the Investment Accelerator," Journal of Political Economy, University of Chicago Press, vol. 97(2), pages 251-87, April.
  13. Geraci, Vincent J, 1977. "Estimation of Simultaneous Equation Models with Measurement Error," Econometrica, Econometric Society, vol. 45(5), pages 1243-55, July.
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  15. Peter F. Christoffersen & Francis X. Diebold, 1997. "Cointegration and Long-Horizon Forecasting," NBER Technical Working Papers 0217, National Bureau of Economic Research, Inc.
  16. Holden, Kenneth, 1969. "The Effect of Revisions to Data on Two Econometric Studies," The Manchester School of Economic & Social Studies, University of Manchester, vol. 37(1), pages 23-37, March.
  17. 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.
  18. 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.
  19. Rosanne Cole, 1969. "Data Errors and Forecasting Accuracy," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 47-82 National Bureau of Economic Research, Inc.
  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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