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Cointegration Analysis in the Presence of Outliers

Author

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  • Heino Bohn Nielsen

    (University of Copenhagen Institute of Economics)

Abstract

The effects of innovational outliers and additive outliers in cointegrated vector autoregressions are examined and it is analyzed how outliers can be modelled with dummy variables. Using a Monte Carlo simulation it is illustrated how misspecified dummies may distort inference on the cointegration rank in finite samples. That questions the common practice in applied cointegration analyses of including unrestricted dummy variables to account for large residuals. Instead it is suggested to test the adequacy of a particular specification of dummies prior to determining the cointegration rank. The points are illustrated on a UK money demand data set

Suggested Citation

  • Heino Bohn Nielsen, 2003. "Cointegration Analysis in the Presence of Outliers," Discussion Papers 03-05, University of Copenhagen. Department of Economics.
    • Handle: RePEc:kud:kuiedp:0305
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    File URL: http://www.econ.ku.dk/english/research/publications/wp/2003/0305.pdf/
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    Citations

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    Cited by:

    1. David F. Hendry & Carlos Santos, 2005. "Regression Models with Data‐based Indicator Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(5), pages 571-595, October.
    2. Hans Christian Kongsted & Heino Bohn Nielsen, 2004. "Analysing I(2) Systems by Transformed Vector Autoregressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 379-397, July.
    3. David F. Hendry & Carlos Santos, 2005. "Regression Models with Data-based Indicator Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(5), pages 571-595, October.

    More about this item

    Keywords

    cointegrated VAR; innovational outlier; additive outlier; dummy variables; Monte Carlo;
    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

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