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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

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    1. repec:adr:anecst:y:1986:i:4:p:05 is not listed on IDEAS
    2. 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.
    3. 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.
    4. Kees Jan van Garderen, 1999. "Exact Geometry of Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(1), pages 1-21, January.
    5. 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).
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    2. 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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