A new approach for estimation of long-run relationships in economic analysis using Engle-Granger and artificial intelligence methods
AbstractIn 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by HAL in its series Working Papers with number halshs-00606048.
Date of creation: 16 Jun 2012
Date of revision:
Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00606048
Contact details of provider:
Web page: http://hal.archives-ouvertes.fr/
Cointegration; Non-linear time series analysis; Augmented Engle-Granger; Vector error correction method; Artificial intelligence; Health economics;
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Diks, Cees & Panchenko, Valentyn, 2006.
"A new statistic and practical guidelines for nonparametric Granger causality testing,"
Journal of Economic Dynamics and Control, Elsevier,
Elsevier, vol. 30(9-10), pages 1647-1669.
- Diks, C.G.H. & Panchenko, V., 2004. "A new statistic and practical guidelines for nonparametric Granger causality testing," CeNDEF Working Papers 04-11, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- Erkan Erdil & I. Hakan Yetkiner, 2009. "The Granger-causality between health care expenditure and output: a panel data approach," Applied Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 41(4), pages 511-518.
- Rivera, Berta & Currais, Luis, 1999. "Income Variation and Health Expenditure: Evidence for OECD Countries," Review of Development Economics, Wiley Blackwell, Wiley Blackwell, vol. 3(3), pages 258-67, October.
- Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, Econometric Society, vol. 55(2), pages 251-76, March.
- Nancy Devlin & Paul Hansen, 2001. "Health care spending and economic output: Granger causality," Applied Economics Letters, Taylor & Francis Journals, Taylor & Francis Journals, vol. 8(8), pages 561-564.
- Gerdtham, Ulf-G. & Jonsson, Bengt, 2000. "International comparisons of health expenditure: Theory, data and econometric analysis," Handbook of Health Economics, Elsevier, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 1, pages 11-53 Elsevier.
- Berta Rivera & Luis Currais, 1999. "Economic growth and health: direct impact or reverse causation?," Applied Economics Letters, Taylor & Francis Journals, Taylor & Francis Journals, vol. 6(11), pages 761-764.
- Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, Elsevier, vol. 16(1), pages 121-130, May.
- Selma J. Mushkin, 1962. "Health as an Investment," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 70, pages 129.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD).
If references are entirely missing, you can add them using this form.