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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. Swamy

    ()

  • George 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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Suggested Citation

  • Stephen Hall & P. Swamy & George Tavlas, 2012. "Generalized cointegration: a new concept with an application to health expenditure and health outcomes," Empirical Economics, Springer, vol. 42(2), pages 603-618, April.
  • Handle: RePEc:spr:empeco:v:42:y:2012:i:2:p:603-618
    DOI: 10.1007/s00181-011-0483-y
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    References listed on IDEAS

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    1. Basmann, R. L., 1988. "Causality tests and observationally equivalent representations of econometric models," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 69-104.
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    4. Zellner, Arnold, 1988. "Causality and causal laws in economics," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 7-21.
    5. Charles I. Jones, 2005. "More life vs. more goods: explaining rising health expenditures," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue may27.
    6. 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.
    7. Robert E. Hall & Charles I. Jones, 2007. "The Value of Life and the Rise in Health Spending," The Quarterly Journal of Economics, Oxford University Press, vol. 122(1), pages 39-72.
    8. 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.
    9. Zellner, Arnold, 1979. "Causality and econometrics," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 10(1), pages 9-54, January.
    10. 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.
    11. Swamy P. A. V. B. & Tavlas George S & Hall Stephen G. F. & Hondroyiannis George, 2010. "Estimation of Parameters in the Presence of Model Misspecification and Measurement Error," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-35, May.
    12. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    13. P. Swamy & George Tavlas, 2007. "The New Keynesian Phillips Curve and Inflation Expectations: Re-Specification and Interpretation," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 31(2), pages 293-306, May.
    14. Jonathan Skinner & Douglas Staiger, 2015. "Technology Diffusion and Productivity Growth in Health Care," The Review of Economics and Statistics, MIT Press, vol. 97(5), pages 951-964, December.
    15. I-Lok Chang & P.A.V.B. Swamy & Charles Hallahan & George S. Tavlas, 2000. "A Computational Approach to Finding Causal Economic Laws," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 105-136, October.
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    Citations

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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. P.A.V.B. Swamy & George S. Tavlas & Stephen G. Hall, 2015. "On the Interpretation of Instrumental Variables in the Presence of Specification Errors," Econometrics, MDPI, Open Access Journal, vol. 3(1), pages 1-10, January.
    4. Gallet, Craig A. & Doucouliagos, Hristos, 2017. "The impact of healthcare spending on health outcomes: A meta-regression analysis," Social Science & Medicine, Elsevier, vol. 179(C), pages 9-17.
    5. 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.

    More about this item

    Keywords

    Generalized cointegration; Non-stationarity; Time-varying coefficient model; Coefficient driver; C130; C190; C220;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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