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Business surveys modelling with seasonal-cyclical long memory models

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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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Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number b08035.

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Length: 11 pages
Date of creation: May 2008
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Handle: RePEc:mse:cesdoc:b08035

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Keywords: Euro area; nowcasting; business surveys; seasonal; long memory.;

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  1. Laurent Ferrara & Dominique Guegan & Zhiping Lu, 2008. "Testing fractional order of long memory processes : a Monte Carlo study," Post-Print halshs-00259193, HAL.
  2. 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.
  3. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages C25-C44, February.
  4. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
  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. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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, HAL.
  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.
  13. Laurent Ferrara, 2007. "Point and interval nowcasts of the Euro area IPI," Applied Economics Letters, Taylor & Francis Journals, vol. 14(2), pages 115-120.
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Cited by:
  1. Artiach, Miguel, 2012. "Leverage, skewness and amplitude asymmetric cycles," MPRA Paper 41267, University Library of Munich, Germany.
  2. 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).

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