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Power and Bias of Likelihood Based Inference in the Cointegration Model under Fractional Cointegration

Author

Listed:
  • Andersson, Michael K.

    (Dept. of Economic Statistics, Stockholm School of Economics)

  • Gredenhoff, Mikael P.

    (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

This paper investigates how fractional cointegration affects the common maximum likelihood cointegration procedure. It is shown that the likelihood ratio test of no cointegration has considerable power against fractional alternatives. In contrast to the case of a cointegrated system, the usual maximum likelihood estimator gives severely biased estimates of the long-run relation under fractional cointegration. This suggests that the standard likelihood approach should be used with caution and that a test to separate fractionally cointegrated series from series that are cointegrated of an integer order should be executed prior to estimation.

Suggested Citation

  • Andersson, Michael K. & Gredenhoff, Mikael P., 1998. "Power and Bias of Likelihood Based Inference in the Cointegration Model under Fractional Cointegration," SSE/EFI Working Paper Series in Economics and Finance 221, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0221
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    Cited by:

    1. Lyhagen, Johan, 1998. "Maximum likelihood estimation of the multivariate fractional cointegrating model," SSE/EFI Working Paper Series in Economics and Finance 233, Stockholm School of Economics.

    More about this item

    Keywords

    Error correction; likelihood ratio test; maximum likelihood; fractional integration;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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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