Business surveys modelling with Seasonal-Cyclical Long Memory models

Laurent Ferrara () and Dominique Guegan ()
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Laurent Ferrara: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, DGEI-DAMEP - Banque de France
Dominique Guegan: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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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.

Keywords: Euro area; nowcasting; business surveys; seasonal; long memory.; long memory; Enquêtes; saisonnalité; longue mémoire; prévision en temps réel. (search for similar items in EconPapers)
Date: 2008-05
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00277379v1
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Citations: View citations in EconPapers (2)

Published in 2008

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