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A simple approach to robust inference in a cointegrating system

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  • Jonathan H. Wright

Abstract

Cointegration requires all the variables in the system to have exact unit roots; accordingly it is conventional for researchers to test for a unit root in each variable prior to a cointegration analysis. Unfortunately, these unit root tests are not powerful. Meanwhile, conventional cointegration methods are not at all robust to slight violations of the requirement that each variable have a unit root. In this paper I show how this difficulty may be circumvented by instrumenting the regressors in the cointegrating regression by deterministic polynomial time trends or by artificially generated random walks.

Suggested Citation

  • Jonathan H. Wright, 1999. "A simple approach to robust inference in a cointegrating system," International Finance Discussion Papers 654, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:654
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    References listed on IDEAS

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    1. King, Robert G., 1988. "Money demand in the United States: A quantitative review," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 29(1), pages 169-172, January.
    2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. Wright, Jonathan H, 2000. "Confidence Sets for Cointegrating Coefficients Based on Stationarity Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 211-222, April.
    4. Lucas, Robert E., 1988. "Money demand in the United States: A quantitative review," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 29(1), pages 137-167, January.
    5. Milton Friedman & Anna Jacobson Schwartz, 1970. "Monetary Statistics of the United States: Estimates, Sources, Methods," NBER Books, National Bureau of Economic Research, Inc, number frie70-1.
    6. Jean-Marie Dufour, 1997. "Some Impossibility Theorems in Econometrics with Applications to Structural and Dynamic Models," Econometrica, Econometric Society, vol. 65(6), pages 1365-1388, November.
    7. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    8. Hoffman, Dennis L & Rasche, Robert H, 1991. "Long-Run Income and Interest Elasticities of Money Demand in the United States," The Review of Economics and Statistics, MIT Press, vol. 73(4), pages 665-674, November.
    9. Stock, James H & Watson, Mark W, 1993. "A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems," Econometrica, Econometric Society, vol. 61(4), pages 783-820, July.
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    11. Cavanagh, Christopher L. & Elliott, Graham & Stock, James H., 1995. "Inference in Models with Nearly Integrated Regressors," Econometric Theory, Cambridge University Press, vol. 11(05), pages 1131-1147, October.
    12. Graham Elliott, 1998. "On the Robustness of Cointegration Methods when Regressors Almost Have Unit Roots," Econometrica, Econometric Society, vol. 66(1), pages 149-158, January.
    13. Peter C.B. Phillips & Bruce E. Hansen, 1988. "Statistical Inference in Instrumental Variables," Cowles Foundation Discussion Papers 869R, Cowles Foundation for Research in Economics, Yale University, revised Apr 1989.
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    Cited by:

    1. Michael Jansson & Marcelo J. Moreira, 2006. "Optimal Inference in Regression Models with Nearly Integrated Regressors," Econometrica, Econometric Society, vol. 74(3), pages 681-714, May.

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    Keywords

    Cointegration ; Statistics;

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