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Generalized cointegration: a new concept with an application to health expenditure and health outcomes

  • Stephen Hall

    ()

  • P. Swamy

    ()

  • George Tavlas

    ()

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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File URL: http://hdl.handle.net/10.1007/s00181-011-0483-y
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Article provided by Springer in its journal Empirical Economics.

Volume (Year): 42 (2012)
Issue (Month): 2 (April)
Pages: 603-618

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Handle: RePEc:spr:empeco:v:42:y:2012:i:2:p:603-618
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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. Charles I. Jones, 2005. "More life vs. more goods: explaining rising health expenditures," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue may27.
  3. 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.
  4. Zellner, Arnold, 1988. "Causality and causal laws in economics," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 7-21.
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  7. 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.
  8. Badi H. Baltagi & Francesco Moscone, 2009. "Health Care Expenditure and Income in the OECD Reconsidered: Evidence from Panel Data," Discussion Papers in Economics 09/5, Department of Economics, University of Leicester.
  9. Robert E. Hall & Charles I. Jones, 2004. "The Value of Life and the Rise in Health Spending," NBER Working Papers 10737, National Bureau of Economic Research, Inc.
  10. Alan M. Garber & Jonathan Skinner, 2008. "Is American Health Care Uniquely Inefficient?," NBER Working Papers 14257, National Bureau of Economic Research, Inc.
  11. 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.
  12. Swamy, P. A. V. B. & Tinsley, P. A., 1980. "Linear prediction and estimation methods for regression models with stationary stochastic coefficients," Journal of Econometrics, Elsevier, vol. 12(2), pages 103-142, February.
  13. 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.
  14. 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.
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