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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.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 654.

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Date of creation: 1999
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Handle: RePEc:fip:fedgif:654

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Keywords: Cointegration ; Statistics;

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  1. 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.
  2. James H. Stock, 1991. "Confidence Intervals for the Largest Autoresgressive Root in U.S. Macroeconomic Time Series," NBER Technical Working Papers 0105, National Bureau of Economic Research, Inc.
  3. Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
  4. Dennis Hoffman & Robert H. Rasche, 1989. "Long-run Income and Interest Elasticities of Money Demand in the United States," NBER Working Papers 2949, National Bureau of Economic Research, Inc.
  5. Denis Kwiatkowski & Peter C.B. Phillips & Peter Schmidt, 1991. "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?," Cowles Foundation Discussion Papers 979, Cowles Foundation for Research in Economics, Yale University.
  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. 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.
  8. 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 211-22, April.
  9. 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.
  10. James H. Stock & Mark W. Watson, 1991. "A simple estimator of cointegrating vectors in higher order integrated systems," Working Paper Series, Macroeconomic Issues 91-3, Federal Reserve Bank of Chicago.
  11. 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.
  12. 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, July.
  13. 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.
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Cited by:
  1. Michael Jansson & Marcelo J. Moreira, 2004. "Optimal Inference in Regression Models with Nearly Integrated Regressors," NBER Technical Working Papers 0303, National Bureau of Economic Research, Inc.

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