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A State Space Approach to Extracting the Signal From Uncertain Data

  • Alastair Cunningham
  • Jana Eklund
  • Chris Jeffery
  • George Kapetanios
  • Vincent Labhard

Most macroeconomic data are uncertain—they are estimates rather than perfect measures of underlying economic variables. 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 for extracting the signal from uncertain data. It describes a two-step estimation procedure in which the history of past revisions is first used to estimate the parameters of a measurement equation describing the official published estimates. These parameters are then imposed in a maximum likelihood estimation of a state space model for the macroeconomic variable.

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Article provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.

Volume (Year): 30 (2009)
Issue (Month): 2 (March)
Pages: 173-180

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Handle: RePEc:taf:jnlbes:v:30:y:2009:i:2:p:173-180
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  1. Kevin Lee & Emi Mise & Kalvinder Shields & Tony Garratt, 2005. "Real time Representations of the Output Gap," Money Macro and Finance (MMF) Research Group Conference 2005 26, Money Macro and Finance Research Group.
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  7. 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.
  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. 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.
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  11. 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.
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