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A new approach for estimation of long-run relationships in economic analysis using Engle-Granger and artificial intelligence methods

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Author Info

  • Arshia Amiri

    ()
    (Department of Agricultural Economics - Shiraz University)

  • Ulf-G Gerdtham

    (Lund University - Lund University)

  • Bruno Ventelou

    ()
    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - Université de la Méditerranée - Aix-Marseille II - Université Paul Cézanne - Aix-Marseille III - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - CNRS : UMR6579)

Abstract

In time series analysis, most estimation of relationships and tests are typically based on linear estimators and most classical co-integration methods and causality tests are based on OLS regresses. However the linear functional specification is not necessarily the most appropriate form. This paper breaks the ordinary rules in econometrics and makes use of time series with artificial intelligence methods, testing for existence of nonlinear relationship. We illustrate the testing exercise using two examples based on OECD health data. In our illustration we confirm that improved nonlinear AEG and VEC, significantly, have a better ability to identify long run co-integration and causal relationships than ordinary linear ones. Ordinary methods and improved-nonlinear methods demonstrate similar results if the variables in a model are approximately linear.

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Bibliographic Info

Paper provided by HAL in its series Working Papers with number halshs-00606048.

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Date of creation: 16 Jun 2012
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Handle: RePEc:hal:wpaper:halshs-00606048

Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00606048
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Related research

Keywords: Cointegration; Non-linear time series analysis; Augmented Engle-Granger; Vector error correction method; Artificial intelligence; Health economics;

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References

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  1. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
  2. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
  3. Erkan Erdil & I. Hakan Yetkiner, 2009. "The Granger-causality between health care expenditure and output: a panel data approach," Applied Economics, Taylor & Francis Journals, vol. 41(4), pages 511-518.
  4. 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.
  5. Rivera, Berta & Currais, Luis, 1999. "Income Variation and Health Expenditure: Evidence for OECD Countries," Review of Development Economics, Wiley Blackwell, vol. 3(3), pages 258-67, October.
  6. Nancy Devlin & Paul Hansen, 2001. "Health care spending and economic output: Granger causality," Applied Economics Letters, Taylor & Francis Journals, vol. 8(8), pages 561-564.
  7. Berta Rivera & Luis Currais, 1999. "Economic growth and health: direct impact or reverse causation?," Applied Economics Letters, Taylor & Francis Journals, vol. 6(11), pages 761-764.
  8. Gerdtham, Ulf-G. & Jonsson, Bengt, 2000. "International comparisons of health expenditure: Theory, data and econometric analysis," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 1, pages 11-53 Elsevier.
  9. Selma J. Mushkin, 1962. "Health as an Investment," Journal of Political Economy, University of Chicago Press, vol. 70, pages 129.
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Cited by:
  1. Amiri, Arshia & Zibaei, Mansour, 2012. "Granger causality between energy use and economic growth in France with using geostatistical models," MPRA Paper 36357, University Library of Munich, Germany.

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