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Generalized Cointegration: A New Concept with an Application to Health Expenditure and Health Outcomes

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  • Stephen Hall

    ()

  • P. A. V. B. Swamy

    ()

  • George S. Tavlas

    ()

Abstract

We propose a new generalization of the concept of cointegration that allows for the possibility that a set of variables are involved in an unknown nonlinear relationship. Although these variables may be unit-root non-stationary, there exists a nonlinear combination of them that takes account of such non-stationarity. We then introduce an estimation technique that allows us to test for the presence of this generalized cointegration in the absence of knowledge as to the true nonlinear functional form and the full set of regressors. We outline the basic stages of the technique and discuss how the issue of unit-root non-stationarity and cointegration affects each stage of the estimation procedure. We then apply this technique to the relationship between health expenditure and health outcomes, which is an important but controversial issue. A number of studies have found very little or no relationship between the level of health expenditure and outcomes. In econometric terms, if there is such a relationship then there should exist a cointegrating relationship between these two variables and possibly many others. The problem that arises is that we may be either unable to measure these other variables or that we do not know about them, in which case we may incorrectly find no relationship between health expenditures and outcomes. We then apply the concept of generalized cointegration; we obtain a highly significant relationship between health expenditure and health outcomes.

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Bibliographic Info

Paper provided by Department of Economics, University of Leicester in its series Discussion Papers in Economics with number 11/22.

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Date of creation: Mar 2011
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Handle: RePEc:lec:leecon:11/22

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Keywords: Generalized cointegration; non-stationarity; time-varying coefficient model; coefficient driver;

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References

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  1. Katherine Baicker & Amitabh Chandra, 2004. "The Productivity of Physician Specialization: Evidence from the Medicare Program," American Economic Review, American Economic Association, vol. 94(2), pages 357-361, May.
  2. P.A.V.B. Swamy & George S. Tavlas & Stephen G. Hall & George Hondroyiannis, 2008. "Estimation of Parameters in the Presence of Model misspecification and Measurement Error," Discussion Papers in Economics 08/27, Department of Economics, University of Leicester.
  3. Alan M. Garber & Jonathan Skinner, 2008. "Is American Health Care Uniquely Inefficient?," NBER Working Papers 14257, National Bureau of Economic Research, Inc.
  4. Baltagi, Badi H. & Moscone, Francesco, 2010. "Health care expenditure and income in the OECD reconsidered: Evidence from panel data," Economic Modelling, Elsevier, vol. 27(4), pages 804-811, July.
  5. Robert E Hall & Charles I Jones, 2007. "The Value of Life and the Rise in Health Spending," The Quarterly Journal of Economics, MIT Press, vol. 122(1), pages 39-72, 02.
  6. P.A.V.B. Swamy & P.A. Tinsley, 1976. "Linear prediction and estimation methods for regression models with stationary stochastic coefficients," Special Studies Papers 78, Board of Governors of the Federal Reserve System (U.S.).
  7. I-Lok Chang & P.A.V.B. Swamy & Charles Hallahan & George S. Tavlas, 2000. "A Computational Approach to Finding Causal Economic Laws," Computational Economics, Society for Computational Economics, vol. 16(1/2), pages 105-136, October.
  8. Basmann, R. L., 1988. "Causality tests and observationally equivalent representations of econometric models," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 69-104.
  9. Zellner, A., 1988. "Causality And Causal Laws In Economics," Papers m8801, Southern California - Department of Economics.
  10. George S. Tavlas & P.A.V.B. Swamy, 2006. "The New Keynesian Phillips Curve and Inflation Expectations: Re-Specification and Interpretation," Working Papers 34, Bank of Greece.
  11. Jonathan Skinner & Douglas Staiger, 2009. "Technology Diffusion and Productivity Growth in Health Care," NBER Working Papers 14865, National Bureau of Economic Research, Inc.
  12. Charles I. Jones, 2005. "More life vs. more goods: explaining rising health expenditures," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue may27.
  13. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  14. Swamy, P.A.V.B. & Yaghi, Wisam & Mehta, Jatinder S. & Chang, I-Lok, 2007. "Empirical best linear unbiased prediction in misspecified and improved panel data models with an application to gasoline demand," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3381-3392, April.
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Cited by:
  1. Stephen G. Hall & P.A.V.B. Swamy & George S. Tavlas, 2012. "Milton Friedman, the demand for money, and the ECB’s monetary policy strategy," Review, Federal Reserve Bank of St. Louis, issue May, pages 153-186.
  2. Hall, Stephen G. & Hondroyiannis, George & Kenjegaliev, Amangeldi & Swamy, P.A.V.B. & Tavlas, George S., 2013. "Is the relationship between prices and exchange rates homogeneous?," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 411-438.
  3. Paleologos, John M. & Polemis, Michael L., 2013. "What drives investment in the telecommunications sector? Some lessons from the OECD countries," Economic Modelling, Elsevier, vol. 31(C), pages 49-57.

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