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Bias correcting adjustment coefficients in a cointegrated VAR with known cointegrating vectors

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  • Kees Jan van Garderen
  • H. Peter Boswijk

Abstract

The maximum likelihood estimator of the adjustment coefficient in a cointegrated vector autoregressive model (CVAR) is generally biased. For the case where the cointegrating vector is known in a first-order CVAR with no intercept, we derive a condition for the unbiasedness of the maximum likelihood estimator of the adjustment coefficients, and provide a simple characterization of the bias in case this condition is violated. A feasible bias correction method is shown to virtually eliminate the bias over a large part of the parameter space.

Suggested Citation

  • Kees Jan van Garderen & H. Peter Boswijk, 2013. "Bias correcting adjustment coefficients in a cointegrated VAR with known cointegrating vectors," UvA-Econometrics Working Papers 13-05, Universiteit van Amsterdam, Dept. of Econometrics.
    • Handle: RePEc:ame:wpaper:1305
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    References listed on IDEAS

    as
    1. Kees Jan van Garderen, 1999. "Exact Geometry of Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(1), pages 1-21, January.
    2. repec:adr:anecst:y:1986:i:4:p:05 is not listed on IDEAS
    3. van GARDEREN, Kees Jan, 1997. "Exact geometry of explosive autoregressive models," LIDAM Discussion Papers CORE 1997068, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Jan R. Magnus, 1986. "The Exact Moments of a Ratio of Quadratic Forms in Normal Variables," Annals of Economics and Statistics, GENES, issue 4, pages 95-109.
    5. Karim M. Abadir & Kaddour Hadri & Elias Tzavalis, 1999. "The Influence of VAR Dimensions on Estimator Biases," Econometrica, Econometric Society, vol. 67(1), pages 163-182, January.
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    Cited by:

    1. Luca Nocciola, 2022. "Finite Sample Forecast Properties and Window Length Under Breaks in Cointegrated Systems," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 167-196, Emerald Group Publishing Limited.

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