Semiconductor industry cycles: Explanatory factors and forecasting

Mathilde Aubry () and Patricia Renou-Maissant ()
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Mathilde Aubry: METIS - Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols - UPMC - Université Pierre et Marie Curie - Paris 6 - EPHE - École Pratique des Hautes Études - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Patricia Renou-Maissant: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique, EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique

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Abstract: This paper aims to suggest the best forecasting model for the semiconductor market. A wide range of alternative modern econometric modeling approaches have been implemented, and a large variety of criteria and tests have been employed to assess the out-of-sample forecasting accuracy at various horizons. The results suggest that if a VECM can be an interesting source of information, the Bayesian models are superior forecasting tools compared to univariate and unrestricted VAR models. However, for decision makers a spectral method could be a useful tool, which can be easily implemented. In addition, MS-AR models make it possible to obtain valuable forecasts on turning-points in order to adjust the programming of heavy capital and research investments.

Keywords: Univariate and multivariate models; semiconductor industry; forecasting accuracy; industry cycles (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

Published in Economic Modelling, 2014, 39, pp.221-231. ⟨10.1016/j.econmod.2014.02.039⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-01101975

DOI: 10.1016/j.econmod.2014.02.039

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