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Asymptotic efficiency of the ordinary least-squares estimator for sur models with integrated regressors

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  • Shin, Dong Wan
  • Joon Kim, Han
  • Jhee, Won-Chul
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    Abstract

    For seemingly unrelated regression (SUR) models with integrated regressors, two sufficient conditions are identified, under which the ordinary least-squares estimator (OLSE) is asymptotically efficient. The first condition is that every pair of regressor processes are cointegrated in a specific way that one regressor is a linear combination of the other regressor up to a zero-mean stationary error and the second condition is that, for every pair of regressor processes, the pair of error processes deriving the regressor processes have zero long-run covariance.

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    File URL: http://www.sciencedirect.com/science/article/B6V1D-4KF770W-1/2/716f503de797fd5e77ee55e0cb88468c
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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 77 (2007)
    Issue (Month): 1 (January)
    Pages: 75-82

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    Handle: RePEc:eee:stapro:v:77:y:2007:i:1:p:75-82

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    Related research

    Keywords: Cointegration Efficiency Generalized least-squares estimator Long-run covariance;

    References

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    1. Phillips, Peter C B & Hansen, Bruce E, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," Review of Economic Studies, Wiley Blackwell, vol. 57(1), pages 99-125, January.
    2. Baltagi, Badi H., 1988. "The Efficiency of OLS in a Seemingly Unrelated Regressions Model," Econometric Theory, Cambridge University Press, vol. 4(03), pages 536-537, December.
    3. Shin, Dong Wan & Oh, Man Suk, 2002. "Asymptotic Efficiency Of The Ordinary Least Squares Estimator For Regressions With Unstable Regressors," Econometric Theory, Cambridge University Press, vol. 18(05), pages 1121-1138, October.
    4. Walter Torous & Rossen Valkanov & Shu Yan, 2004. "On Predicting Stock Returns with Nearly Integrated Explanatory Variables," The Journal of Business, University of Chicago Press, vol. 77(4), pages 937-966, October.
    5. Krämer, Walter & Hassler, Uwe, 1997. "Limiting efficiency of OLS vs. GLS when regressors are fractionally integrated," Technical Reports 1997,01, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    6. Shin, Dong Wan & Oh, Man-Suk, 2004. "Fully modified semiparametric GLS estimation for regressions with nonstationary seasonal regressors," Journal of Econometrics, Elsevier, vol. 122(2), pages 247-280, October.
    7. Li, Kai, 1999. "Testing Symmetry and Proportionality in PPP: A Panel-Data Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 409-18, October.
    8. Moon, Hyungsik R., 1999. "A note on fully-modified estimation of seemingly unrelated regressions models with integrated regressors," Economics Letters, Elsevier, vol. 65(1), pages 25-31, October.
    9. 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.
    10. Peter C.B. Phillips & Steven N. Durlauf, 1985. "Multiple Time Series Regression with Integrated Processes," Cowles Foundation Discussion Papers 768, Cowles Foundation for Research in Economics, Yale University.
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