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Business surveys modelling with Seasonal-Cyclical Long Memory models

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Author Info
Laurent Ferrara () (Banque de France and CES, University Paris 1 - Panthéon - Sorbonne)
Dominique Guégan () (CES, University Paris 1 - Panthéon - Sorbonne)

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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 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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File URL: http://economicsbulletin.vanderbilt.edu/2008/volume3/EB-08C20041A.pdf
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Publisher Info
Article provided by Economics Bulletin in its journal Economics Bulletin.

Volume (Year): 3 (2008)
Issue (Month): 29 ()
Pages: 1-10
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:ebl:ecbull:v:3:y:2008:i:29:p:1-10

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Postal: Economics Bulletin, Department of Economics, 414 Calhoun Hall, Vanderbilt University, Nashville TN 37235, USA
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Related research
Keywords: business surveys; seasonality; long memory models; forecasting.;

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Find related papers by JEL classification:
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
C5 - Mathematical and Quantitative Methods - - Econometric Modeling

References listed on IDEAS
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  1. Sofia C. Olhede, 2004. "Large-sample properties of the periodogram estimator of seasonally persistent processes," Biometrika, Oxford University Press for Biometrika Trust, vol. 91(3), pages 613-628, September.
  2. Angelini, Elena & Camba-Mendez, Gonzalo & Giannone, Domenico & Reichlin, Lucrezia & Rünstler, Gerhard, 2008. "Short-term Forecasts of Euro Area GDP Growth," CEPR Discussion Papers 6746, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  3. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July. [Downloadable!] (restricted)
  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_v1, HAL. [Downloadable!]
  5. 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. [Downloadable!]
    Other versions:
  6. Laurent Ferrara, 2007. "Point and interval nowcasts of the Euro area IPI," Applied Economics Letters, Taylor and Francis Journals, vol. 14(2), pages 115-120. [Downloadable!] (restricted)
  7. Wilfredo Palma & Ngai Hang Chan, 2005. "Efficient Estimation of Seasonal Long-Range-Dependent Processes," Journal of Time Series Analysis, Blackwell Publishing, vol. 26(6), pages 863-892, November. [Downloadable!] (restricted)
  8. 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. [Downloadable!] (restricted)
  9. Luis A. Gil-alana, 2006. "Testing Seasonality in the Context of Fractionally Integrated Processes," Annales d'Economie et de Statistique, ADRES, issue 81, pages 03, Janvier-M. [Downloadable!]
  10. 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. [Downloadable!] (restricted)
  11. 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.
  12. 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. [Downloadable!]
  13. Laurent Ferrara & Dominique Guegan, 2006. "Fractional seasonality: Models and Application to Economic Activity in the Euro Area," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00185370_v1, HAL. [Downloadable!]
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