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A state space approach to extracting the signal from uncertain data

Author

Listed:
  • Alastair Cunningham
  • Jana Eklund
  • Christopher Jeffery
  • George Kapetanios
  • Vincent Labhard

Abstract

Most macroeconomic data are uncertain - they are estimates rather than perfect measures. Use of these uncertain data to form an assessment of current activity can be viewed as a problem of signal extraction. One symptom of that uncertainty is the propensity of statistical agencies to revise their estimates in the light of new information or methodological advances. This paper sets out an approach to extracting the signal from uncertain data that takes the experience of past revisions as representative of the uncertainties surrounding the latest published estimates. Specifically, it describes a two-step estimation procedure in which the history of past revisions (real-time data) are first used to estimate the parameters of a measurement equation describing the official published estimates; and these parameters are then imposed in a maximum likelihood estimation of a state space representation of the 'true' profile of the macroeconomic variable.

Suggested Citation

  • 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.
  • Handle: RePEc:boe:boeewp:336
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    References listed on IDEAS

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    1. Donald, Stephen G & Newey, Whitney K, 2001. "Choosing the Number of Instruments," Econometrica, Econometric Society, vol. 69(5), pages 1161-1191, September.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    4. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2008. "Real-Time Representations of the Output Gap," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 792-804, November.
    5. 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.
    6. Patterson, K. D., 1994. "A state space model for reducing the uncertainty associated with preliminary vintages of data with an application to aggregate consumption," Economics Letters, Elsevier, vol. 46(3), pages 215-222, November.
    7. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, May.
    8. George Kapetanios & Tony Yates, 2004. "Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models," Working Papers 520, Queen Mary University of London, School of Economics and Finance.
    9. Anthony Garratt & Shaun P Vahey, 2006. "UK Real-Time Macro Data Characteristics," Economic Journal, Royal Economic Society, vol. 116(509), pages 119-135, February.
    10. 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.
    11. 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.
    12. Jan Jacobs & Jan-Egbert Sturm, 2007. "A real-time analysis of the Swiss trade account," Money Macro and Finance (MMF) Research Group Conference 2006 167, Money Macro and Finance Research Group.
    13. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    14. George Kapetanios, 2004. "Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models," Working Papers 520, Queen Mary University of London, School of Economics and Finance.
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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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