Production Forecasting of Taiwan's Technology Industrial Cluster: A Bayesian Autoregression Approach

Canadian Journal of Administrative Sciences, Jun 2005 by Lee, Jack C, Wang, Chi-Hsiu, Hsu, Po-Hsuan, Lai, Hsien-Che

Abstract

This study proposes a forecasting method that combines the clustering effect and non-informative diffuse-prior Bayesian vector autoregression (NDBVAR) model to forecast the productions of technology industries. Two empirical cases are examined to verify the proposed method: the semiconductor industry and computer manufacturing industry in Taiwan. It is found that the NDBVAR model outperforms the other three conventional time series models including the autoregression (AR), vector autoregression (VAR), and Litterman Bayesian VAR (LBVAR) models. Moreover, the NDBVAR model also outperforms the forecast reports from leading market information providers over the past several years. The forecasting method proposed is therefore concluded to be a feasible approach for production prediction, especially for technology industries in volatile environments.

JEL Classification: C32, C53, E27

Keywords: industrial clusters, vector autoregression, Bayesian vector autoregression, forecasting, Taiwan.

Résumé

La présente étude propose une méthode prévisionnelle qui combine les effets de regroupement et le non-informative diffuse-prior Bayesian vector autoregression model (NDBVAR) pour prévoir les productions des industries de technologie. Pour évaluer la méthode proposée, l'étude examine deux cas empiriques : les industries taiwanaises du semiconducteur et de fabrication d'ordinateur. Elle révèle que le modèle NDBVAR est plus performant que les trois modèles conventionnels en série chronologique notamment le modèle d'autoregression (AR), le modèle de vecteur d'autoregression (VAR), et le modèle Litterman Bayesian (LBVAR). L'étude montre aussi qu'au cours des dernières années, les modèles NDBVAR ont été plus performants que les rapports prévisionnels des prestataires d'informations qui dominent le marché. Elle débouche sur la constatation que la méthode prévisionnelle proposée est une approche réalisable pour la prévision de la production, en particulier pour les industries de la technologie dans un environnement volatile.

Mots clés : grappes industrielles, vecteur d'autorégression, Bayesian vector autorégression, prévision, Taiwan.

The development of technology industries is one of the main subjects in contemporary business research. The perspective of a specific technology industry affects investment plans of private sectors and science and technology policies of governments. Production forecasting is a burgeoning topic in technology management, which aims to assist decision makers in technology industries that are exposed to numerous uncertainties including volatile fluctuations, sudden skyrocketing growth, and unexpected slumps in market. In the literature, the time series model class was one of the most popular prediction methodologies in previous decades. Some pioneer studies have attempted to provide predictive methods for production forecasting of technology industries (e.g., Chang, Lai, & Yu, 2005; Hsu, Wang, Shyu, & Yu, 2003; Tseng, Tzeng, & Yu, 1999). However, those prognostic techniques are still far from satisfactory at this time, and more exploration is needed.

We start our exploration in developing a new forecasting method for technology industries by meditating on the following questions: Which models have been studied in the literature? Can we propose a model with better features in handling the unstable dynamics and discrete shocks? Using that model, what variables could be considered to produce better prediction?

First, we observed that various time series models have been used to predict industrial productions (e.g., Hsu et al., 2003; Marchetti & Parigi, 2000; Simpson, Osborn, & Sensier, 2001; Tseng et al., 1999). Second, we looked for a Bayesian multivariate time series model that fits unsteady environments better than traditional frequency-based models, and found that the non-informative diffuse-prior Bayesian vector autoregression (NDBVAR) model has good features: its prior is flexible and its computation is efficient. It is therefore expected to provide more precise short-term forecasting for production of technology industries. Third, since industrial clustering has been regarded as a crucial driver in the development of technology industries (Bergeron, Lallich, & Bas, 1998; Gover, 1993; Mathews, 1997; Swann & Prevezer, 1996),1 it can be presumed that the production values of different industries within a specific industrial cluster carry important information regarding the momentum and dynamics between those industries. We followed this rationale and took the production values within an industrial cluster as the endogenous variables in multivariate time series models. After considering all three questions, we were motivated to propose a new forecasting method that is a NDBVAR model based on industrial clustering.

We examined the feasibility of our method by considering two empirical cases of Taiwan's technology industries: the semiconductor industry and the computer manufacturing industry. We had good reasons for considering these two industries. First, in both industries, Taiwan's firms have been main players in global markets over the past 10 years, so our experiments will be meaningful to researchers and practitioners from other countries. Second, a review of the history of these two industries indicates that their prosperity can be attributed to a strong clustering effect within Taiwan (e.g., Chang & Hsu, 1998; Mathews, 1997). To validate our proposition, we checked the predictive abilities of a series of autoregression (AR) systems including univariate AR, vector autoregression (VAR), Litterman BVAR (LBVAR), and NDBVAR models. The results show that, in both industries, the NDBVAR model provides more accurate predictions than all of the other competitive models. Moreover, we found that NDBVAR forecasts offer favourable results in comparison with the forecast reports from leading market information providers in Taiwan: the Industrial Technology Research Institute (ITRI) in the semiconductor industry and the Institute for Information Industry (III) in the computer manufacturing industry. We therefore confirmed that the proposed forecasting method is of practical merit.