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A Spurious Regression Approach to Estimating Structural Parameters

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  • Masao Ogaki

    ()

  • Ling Hu

    ()

  • Chi-Young Choi

    ()

Abstract

Economic models often imply that certain variables are cointegrated. However, tests often fail to reject the null hypothesis of no cointegration for these variables. One possible explanation of these test results is that the error is unit root nonstationary due to a nonstationary measurement error in one variable. For example, currency held by the domestic economic agents for legitimate transactions is very hard to measure due to currency held by foreign residents and black market transactions. Therefore, money may be measured with a nonstationary error. If the money demand function is stable in the long-run, we have a cointegrating regression when money is measured with a stationary measurement error, but have a spurious regression when money is measured with a nonstationary measurement error. We can still recover structural parameters under certain conditions for the nonstationary measurement error. This paper proposes econometric methods based on asymptotic theory to estimate structural parameters with spurious regressions involving unit root nonstaionary variables.

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Bibliographic Info

Paper provided by Ohio State University, Department of Economics in its series Working Papers with number 04-01.

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Date of creation: Jun 2004
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Handle: RePEc:osu:osuewp:04-01

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  1. de Jong, Robert M. & Davidson, James, 2000. "The Functional Central Limit Theorem And Weak Convergence To Stochastic Integrals I," Econometric Theory, Cambridge University Press, vol. 16(05), pages 621-642, October.
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  5. Masao Ogaki & Chi-Young Choi, 2001. "The Gauss-Markov Theorem and Spurious Regressions," Working Papers 01-13, Ohio State University, Department of Economics.
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  12. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
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  14. 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.
  15. Steven N. Durlauf & Peter C.B. Phillips, 1986. "Trends Versus Random Walks in Time Series Analysis," Cowles Foundation Discussion Papers 788, Cowles Foundation for Research in Economics, Yale University.
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  17. Cooley, Thomas F & Ogaki, Masao, 1996. "A Time Series Analysis of Real Wages, Consumption, and Asset Returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 119-34, March-Apr.
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Cited by:
  1. Travaglini, Guido, 2007. "The U.S. Dynamic Taylor Rule With Multiple Breaks, 1984-2001," MPRA Paper 3419, University Library of Munich, Germany, revised 15 Jun 2007.
  2. Badi H. Baltagi & Chihwa Kao & Long Liu, 2007. "Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors: The Case of Stationary and Non-Stationary Regressors and Residuals," Center for Policy Research Working Papers 93, Center for Policy Research, Maxwell School, Syracuse University.

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