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Accelerated Estimation of Switching Algorithms: The Cointegrated VAR Model and Other Applications

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  • Jurgen A. Doornik

    (Economics Department and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, UK)

Abstract

Restricted versions of the cointegrated VAR are usually estimated using switching algorithms. These algorithms alternate between two sets of variables but can be slow to converge. Acceleration methods are proposed that combine simplicity and effectiveness. These methods also outperform existing proposals in some applications of the EM method and PARAFAC.

Suggested Citation

  • Jurgen A. Doornik, 2017. "Accelerated Estimation of Switching Algorithms: The Cointegrated VAR Model and Other Applications," Economics Papers 2017-W05, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:1705
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    References listed on IDEAS

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    1. Ravi Varadhan & Christophe Roland, 2008. "Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 335-353, June.
    2. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
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    5. Doornik, Jurgen A. & O'Brien, R. J., 2002. "Numerically stable cointegration analysis," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 185-193, November.
    6. Mortaza Jamshidian & Robert I. Jennrich, 1997. "Acceleration of the EM Algorithm by using Quasi‐Newton Methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 569-587.
    7. Berlinet, A.F. & Roland, Ch., 2012. "Acceleration of the EM algorithm: P-EM versus epsilon algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4122-4137.
    8. H. Peter Boswijk & Jurgen A. Doornik, 2004. "Identifying, estimating and testing restricted cointegrated systems: An overview," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(4), pages 440-465, November.
    9. Johansen, Soren, 1995. "Identifying restrictions of linear equations with applications to simultaneous equations and cointegration," Journal of Econometrics, Elsevier, vol. 69(1), pages 111-132, September.
    10. Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, number 9780199285679.
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    Cited by:

    1. Jurgen A. Doornik & Rocco Mosconi & Paolo Paruolo, 2017. "Formula I(1) and I(2): Race Tracks for Likelihood Maximization Algorithms of I(1) and I(2) Cointegrated VAR Models," Econometrics, MDPI, vol. 5(4), pages 1-30, November.
    2. Jurgen A. Doornik, 2017. "Maximum Likelihood Estimation of the I(2) Model under Linear Restrictions," Econometrics, MDPI, vol. 5(2), pages 1-20, May.
    3. H. Peter Boswijk & Paolo Paruolo, 2017. "Likelihood Ratio Tests of Restrictions on Common Trends Loading Matrices in I(2) VAR Systems," Econometrics, MDPI, vol. 5(3), pages 1-17, June.

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