Semiconductor industry cycles: Explanatory factors and forecasting

Mathilde Aubry (1) , Patricia Renou-Maissant (2, 3)

1 METIS - Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols
2 CREM - Centre de recherche en économie et management
3 EconomiX - EconomiX

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.

Mots clés: Univariate and multivariate models; semiconductor industry; forecasting accuracy; industry cycles

Domaines: Economies et finances

Mathilde Aubry, Patricia Renou-Maissant. Semiconductor industry cycles: Explanatory factors and forecasting. Economic Modelling, 2014, 39, pp.221-231. ⟨10.1016/j.econmod.2014.02.039⟩. ⟨halshs-01101975⟩