A Multivariate Long Memory Model for the Specification of Real Output in the US, the UK, and Canada
AbstractThis paper deals with a multivariate long memory model for the specification of real output in the US, the UK, and Canada. We examine the orders of integration of the three time series first individually and then allow cross dependence between observations. Performing univariate analysis, results show that the three series have orders of integration higher than 1, especially Canada. The multivariate model supports this view, finding conclusive evidence of non-stationarity for the three series and higher orders of integration for Canada than for the UK or the US. With respect to the cross-dependence structure, it seems that the US and Canada, and the US with the UK present the highest degrees of correlation across countries.
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Bibliographic InfoArticle provided by College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan in its journal International Journal of Business and Economics.
Volume (Year): 6 (2007)
Issue (Month): 2 (August)
multivariate tests; fractional integration; long memory;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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.:
- Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
- Gil-Alana, L. A., 2003. "A fractional multivariate long memory model for the US and the Canadian real output," Economics Letters, Elsevier, vol. 81(3), pages 355-359, December.
- Robinson, P. M., 1991. "Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression," Journal of Econometrics, Elsevier, vol. 47(1), pages 67-84, January.
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