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Forecasting with measurement errors in dynamic models

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  • Richard Harrison
  • George Kapetanios
  • Tony Yates

Abstract

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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Bibliographic Info

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
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Handle: RePEc:boe:boeewp:237

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  2. Evan Koenig & Sheila Dolmas & Jeremy M. Piger, 2002. "The use and abuse of 'real-time' data in economic forecasting," Working Papers 2001-015, Federal Reserve Bank of St. Louis.
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  11. Orphanides, Athanasios, 2000. "The quest for prosperity without inflation," Working Paper Series 0015, European Central Bank.
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  14. Busetti, Fabio, 2004. "Preliminary Data and Econometric Forecasting: An Application with the Bank of Italy Quarterly Model," CEPR Discussion Papers 4382, C.E.P.R. Discussion Papers.
  15. Egginton, Donald & Andreas Pick & Shaun P. Vahey, 2002. "Keep It Real!: A Real-time UK Macro Data Set," Royal Economic Society Annual Conference 2002 69, Royal Economic Society.
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Cited by:
  1. Andrea Silvestrini & Matteo Salto & Laurent Moulin & David Veredas, 2008. "Monitoring and forecasting annual public deficit every month: the case of France," Empirical Economics, Springer, vol. 34(3), pages 493-524, June.
  2. Alastair Cunningham & Jana Eklund & Christopher Jeffery & George Kapetanios & Vincent Labhard, 2007. "A state space approach to extracting the signal from uncertain data," Bank of England working papers 336, Bank of England.
  3. Juan Manuel Julio Román, 2011. "Modeling Data Revisions," BORRADORES DE ECONOMIA 007929, BANCO DE LA REPÚBLICA.
  4. Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.
  5. George Kapetanios & Tony Yates, 2010. "Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 869-893.
  6. Jarkko Jääskelä & Tony Yates, 2005. "Monetary policy and data uncertainty," Bank of England working papers 281, Bank of England.
  7. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
  8. George Kapetanios & Tony Yates, 2004. "Estimating time-variation in measurement error from data revisions; an application to forecasting in dynamic models," Bank of England working papers 238, Bank of England.
  9. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
  10. Paul Downward & Andrew Mearman, 2005. "Methodological Triangulation at the Bank of England:An Investigation," Working Papers 0505, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.

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