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Recursive Estimation in Econometrics

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  • Stephen Pollock

    (Queen Mary, University of London)

Abstract

An account is given of recursive regression and of Kalman filtering which gathers the important results and the ideas that lie behind them within a small compass. It emphasises the areas in which econometricians have made contributions, which include the methods for handling the initial-value problem associated with nonstationary processes and the algorithms of fixed-interval smoothing.

Suggested Citation

  • Stephen Pollock, 2002. "Recursive Estimation in Econometrics," Working Papers 462, Queen Mary University of London, School of Economics and Finance.
  • Handle: RePEc:qmw:qmwecw:462
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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2002/items/wp462.pdf
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    References listed on IDEAS

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    6. G. Gardner & A. C. Harvey & G. D. A. Phillips, 1980. "An Algorithm for Exact Maximum Likelihood Estimation of Autoregressive–Moving Average Models by Means of Kaiman Filtering," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(3), pages 311-322, November.
    7. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
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    Cited by:

    1. Mewael F. Tesfaselassie & Eric Schaling & Sylvester Eijffinger, 2011. "Learning about the Term Structure and Optimal Rules for Inflation Targeting," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(8), pages 1685-1706, December.
    2. Tesfaselassie, M.F., 2005. "Communication, learning and optimal monetary policy," Other publications TiSEM 33c69063-eed7-4938-9f51-e, Tilburg University, School of Economics and Management.

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    More about this item

    Keywords

    Recursive regression; Kalman filtering; Fixed-interval smoothing; The initial-value problem;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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