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Generalized method of moments estimation for cointegrated vector autoregressive models

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  • Park, Suk K.
  • Ahn, Sung K.
  • Cho, Sinsup
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    Abstract

    In this study, a generalized method of moments (GMM) for the estimation of nonstationary vector autoregressive models with cointegration is considered. Two iterative methods are considered: a simultaneous estimation method and a switching estimation method. The asymptotic properties of the GMM estimators of these methods are found to be the same as those of the Gaussian reduced-rank estimator. Through Monte Carlo simulation, the small-sample properties of the GMM estimators are studied and compared with those of the Gaussian reduced-rank estimator and the maximum likelihood estimator considered by other researchers. In the case of small samples, the GMM estimators are more robust to deviations from normality assumptions, particularly to outliers.

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 55 (2011)
    Issue (Month): 9 (September)
    Pages: 2605-2618

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    Handle: RePEc:eee:csdana:v:55:y:2011:i:9:p:2605-2618

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Cointegration GMM estimation VAR model;

    References

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    1. Kim, In-Moo & Park, Joon Y., 2005. "Iterative Maximum Likelihood Estimation of Cointegrating Vectors," Working Papers 2005-02, Rice University, Department of Economics.
    2. Bossaerts, Peter, 1988. "Common nonstationary components of asset prices," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 347-364.
    3. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
    4. Ralf BRUEGGEMANN & Helmut LUETKEPOHL, 2004. "Practical Problems with Reduced Rank ML Estimators for Cointegration Parameters and a Simple Alternative," Economics Working Papers ECO2004/20, European University Institute.
    5. Stock, James H, 1987. "Asymptotic Properties of Least Squares Estimators of Cointegrating Vectors," Econometrica, Econometric Society, vol. 55(5), pages 1035-56, September.
    6. Gonzalo, Jesus, 1994. "Five alternative methods of estimating long-run equilibrium relationships," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 203-233.
    7. Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, number 9780199285679, September.
    8. Ahn, Sung K. & Reinsel, Gregory C., 1994. "Estimation of partially nonstationary vector autoregressive models with seasonal behavior," Journal of Econometrics, Elsevier, vol. 62(2), pages 317-350, June.
    9. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    10. Kitamura, Yuichi & Phillips, Peter C. B., 1997. "Fully modified IV, GIVE and GMM estimation with possibly non-stationary regressors and instruments," Journal of Econometrics, Elsevier, vol. 80(1), pages 85-123, September.
    11. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
    12. Quintos, Carmela E., 1998. "Analysis of cointegration vectors using the GMM approach," Journal of Econometrics, Elsevier, vol. 85(1), pages 155-188, July.
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