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Business surveys modelling with Seasonal-Cyclical Long Memory models

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  • Ferrara, L.
  • Guégan, D.

Abstract

Business surveys are an important element in the analysis of the short-term economic situation because of the timeliness and nature of the information they convey. Especially, surveys are often involved in econometric models in order to provide an early assessment of the current state of the economy, which is of great interest for policy-makers. In this paper, we focus on non-seasonally adjusted business surveys relative to the Euro area released by the European Commission. We introduce an innovative way for modelling those series taking the persistence of the seasonal roots into account through seasonal-cyclical long memory models. We empirically prove that such models produce more accurate forecasts than classical seasonal linear models.

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Bibliographic Info

Paper provided by Banque de France in its series Working papers with number 224.

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Length: 14 pages
Date of creation: 2008
Date of revision:
Handle: RePEc:bfr:banfra:224

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Postal: Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS
Web page: http://www.banque-france.fr/
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Keywords: Euro area ; business surveys ; seasonal ; long memory.;

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References

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  1. repec:hal:cesptp:halshs-00185370 is not listed on IDEAS
  2. Josu Artech & Peter M Robinson, 1998. "Semiparametric Inference in Seasonal and Cyclical Long Memory Processes - (Now published in Journal of Time Series Analysis, 21 (2000), pp.1-25.)," STICERD - Econometrics Paper Series /1998/359, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  3. Laurent Ferrara, 2007. "Point and interval nowcasts of the Euro area IPI," Applied Economics Letters, Taylor & Francis Journals, vol. 14(2), pages 115-120.
  4. Dominique Guegan, 2003. "A prospective study of the k-factor Gegenbauer processes with heteroscedastic errors and an application to inflation rates," Post-Print halshs-00201314, HAL.
  5. Ferrara, Laurent & Guegan, Dominique, 2001. "Forecasting with k-Factor Gegenbauer Processes: Theory and Applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(8), pages 581-601, December.
  6. Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
  7. Elena Angelini & Gonzalo Camba-Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2008. "Short-Term Forecasts of Euro Area GDP Growth," Working Papers ECARES ECARES 2008-035, ULB -- Universite Libre de Bruxelles.
  8. repec:hal:cesptp:halshs-00259193 is not listed on IDEAS
  9. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
  10. Wilfredo Palma & Ngai Hang Chan, 2005. "Efficient Estimation of Seasonal Long-Range-Dependent Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(6), pages 863-892, November.
  11. repec:hal:cesptp:hal-00486655 is not listed on IDEAS
  12. Laurent Ferrara & Dominique Guegan & Zhiping Lu, 2008. "Testing fractional order of long memory processes : a Monte Carlo study," Documents de travail du Centre d'Economie de la Sorbonne b08012, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  13. Sofia C. Olhede, 2004. "Large-sample properties of the periodogram estimator of seasonally persistent processes," Biometrika, Biometrika Trust, vol. 91(3), pages 613-628, September.
  14. Franses, Philip Hans & Ooms, Marius, 1997. "A periodic long-memory model for quarterly UK inflation," International Journal of Forecasting, Elsevier, vol. 13(1), pages 117-126, March.
  15. repec:hal:journl:halshs-00259193 is not listed on IDEAS
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Cited by:
  1. Arteche González, Jesús María & Artiach Escauriaza, Miguel Manuel, 2011. "Doubly fractional models for dynamic heteroskedastic cycles," BILTOKI 2011-03, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
  2. Artiach, Miguel, 2012. "Leverage, skewness and amplitude asymmetric cycles," MPRA Paper 41267, University Library of Munich, Germany.

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