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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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    File URL: http://www.bankofengland.co.uk/research/Documents/workingpapers/2007/WP336.pdf
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    References listed on IDEAS

    as
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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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