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2014
- handle: 10670/1.3snr7q
- halshs: halshs-01101975
- doi: 10.1016/j.econmod.2014.02.039
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info:eu-repo/semantics/altIdentifier/doi/10.1016/j.econmod.2014.02.039
Mots-clés
Univariate and multivariate models semiconductor industry forecasting accuracy industry cycles C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C53 - Forecasting and Prediction Methods • Simulation Methods L - Industrial Organization/L.L6 - Industry Studies: Manufacturing/L.L6.L63 - Microelectronics • Computers • Communications EquipmentSujets proches
Prediction Forecasts FuturologyCiter ce document
Mathilde Aubry et al., « Semiconductor industry cycles: Explanatory factors and forecasting », HAL-SHS : économie et finance, ID : 10.1016/j.econmod.2014.02.039
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Résumé
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.