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A State Space Approach to Extracting the Signal from Uncertain Data

Author

Listed:
  • Alastair Cunningham

    (Bank of England)

  • Jana Eklund

    (Bank of England)

  • Chris Jeffery

    (Bank of England)

  • George Kapetanios

    (Queen Mary, University of London and Bank of England)

  • Vincent Labhard

    (European Central Bank)

Abstract

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 are 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.

Suggested Citation

  • Alastair Cunningham & Jana Eklund & Chris Jeffery & George Kapetanios & Vincent Labhard, 2009. "A State Space Approach to Extracting the Signal from Uncertain Data," Working Papers 637, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:637
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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2009/items/wp637.pdf
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    References listed on IDEAS

    as
    1. Jarkko Jääskelä & Tony Yates, 2005. "Monetary policy and data uncertainty," Bank of England working papers 281, Bank of England.
    2. 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.
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    6. 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.
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    More about this item

    Keywords

    Real-time data analysis; State space models; Data uncertainty; Data revisions;
    All these keywords.

    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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