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Abstract

This paper proposes new iterative reduced-rank regression procedures for seasonal cointegration analysis. The suggested methods are motivated by the idea that modelling the cointegration restrictions jointly at different frequencies may increase efficiency in finite samples. Monte Carlo simulations indicate that the new tests and estimators perform well with respect to already existing statistical procedures.

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File URL: http://www.sciencedirect.com/science/article/B6V8V-4CVR99K-2/2/1688a9919f5974f4a22fb9dedbe47ff4
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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 49 (2005)
Issue (Month): 2 (April)
Pages: 333-348

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Handle: RePEc:eee:csdana:v:49:y:2005:i:2:p:333-348

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Web page: http://www.elsevier.com/locate/csda

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• C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models

References

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   • Franses, Philip Hans & Kunst, Robert M, 1999. " On the Role of Seasonal Intercepts in Seasonal Cointegration," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(3), pages 409-33, August.
   • Franses, Philip Hans & Kunst, Robert M., 1995. "On the role of seasonal intercepts in seasonal cointegration," Economics Series 15, Institute for Advanced Studies.
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10. Granger, C.W.J. & Siklos, P.L., 1993. "Systematic Sampling, Temporal Aggregation, Seasonal Adjustment, and Cointegration: Theory and Evidence," Working Papers 93001, Wilfrid Laurier University, Department of Economics.
    
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11. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 374-86, July.
    
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    • Ghysels, E. & Granger, C.W.J. & Siklos, P.L., 1995. "Is Seasonal Adjustment a Linear or Nonlinear Data Filtring Process," Cahiers de recherche 9517, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
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15. Lee, Hahn Shik, 1992. "Maximum likelihood inference on cointegration and seasonal cointegration," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 1-47.
16. Löf, Mårten & Franses, Philip Hans, 2000. "On Forecasting Cointegrated Seasonal Time Series," Working Paper Series in Economics and Finance 350, Stockholm School of Economics.
17. Franses, Philip Hans & McAleer, Michael, 1998. " Cointegration Analysis of Seasonal Time Series," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 651-78, December.
18. Haug, A.A., 1992. "Tests for Cointegration: A Monte Carlo Comparison," Papers 93-2, York (Canada) - Department of Economics.
    
    • Haug, Alfred A., 1996. "Tests for cointegration a Monte Carlo comparison," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 89-115.
19. Karim M. Abadir & Paolo Paruolo, 1997. "Two Mixed Normal Densities from Cointegration Analysis," Econometrica, Econometric Society, vol. 65(3), pages 671-680, May.
20. Gianluca Cubadda, 2000. "Complex Reduced Rank Models for Seasonally Cointegrated Time Series," Econometric Society World Congress 2000 Contributed Papers 0092, Econometric Society.
    
    • Cubadda, Gianluca, 2001. " Complex Reduced Rank Models for Seasonally Cointegrated Time Series," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 63(4), pages 497-511, September.
21. Abeysinghe, Tilak, 1994. "Deterministic seasonal models and spurious regressions," Journal of Econometrics, Elsevier, vol. 61(2), pages 259-272, April.
22. Lof, Marten & Hans Franses, Philip, 2001. "On forecasting cointegrated seasonal time series," International Journal of Forecasting, Elsevier, vol. 17(4), pages 607-621.

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Citations

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Cited by:

1. Gianluca Cubadda, 2007. "A Unifying Framework for Analysing Common Cyclical Features in Cointegrated Time Series," CEIS Research Paper 102, Tor Vergata University, CEIS.
   
   • Cubadda, Gianluca, 2007. "A unifying framework for analysing common cyclical features in cointegrated time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 896-906, October.
2. Marco Centoni & Gianluca Cubadda, 2011. "Modelling Comovements of Economic Time Series: A Selective Survey," CEIS Research Paper 215, Tor Vergata University, CEIS, revised 26 Oct 2011.
   
   • Marco Centoni & Gianluca Cubadda, 2011. "Modelling comovements of economic time series: a selective survey," Statistica, Department of Statistics, University of Bologna, vol. 71(2), pages 267-294.
3. Fabio Busetti, 2004. "Tests of seasonal integration and cointegration in multivariate unobserved component models," Econometrics 0411003, EconWPA.
   
   • Fabio Busetti, 2006. "Tests of seasonal integration and cointegration in multivariate unobserved component models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 419-438.
   • Fabio Busetti, 2003. "Tests of seasonal integration and cointegration in multivariate unobserved component models," Temi di discussione (Economic working papers) 476, Bank of Italy, Economic Research and International Relations Area.
4. Ozlem Tasseven, 2009. "Seasonal Co-integration An Extension of the Johansen and Schaumburg Approach with an Exclusion Test," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 56(1), pages 39-53, March.
5. Seong, Byeongchan, 2009. "Bonferroni correction for seasonal cointegrating ranks," Economics Letters, Elsevier, vol. 103(1), pages 42-44, April.
6. Gil-Alana, L.A., 2008. "Testing of seasonal integration and cointegration with fractionally integrated techniques: An application to the Danish labour demand," Economic Modelling, Elsevier, vol. 25(2), pages 326-339, March.

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