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

Of course, our results are based on experiments on two empirical cases and may not be generally applicable; however, we do believe that our results from deliberately examining these two cases are credible, and it is fair to say that our forecasting method has merits in at least some circumstances. On the other hand, since our method is based on a commonly used non-informative prior, the predictive advantage of our NDBVAR forecasts is unlikely a result of calibration.

In our view, the variable selection and range forecasting will be two interesting topics waiting for future researchers to explore. Although the variables used in this study are selected by clustering effect, other variables like macroeconomic variables could be very meaningful and are worthy of consideration. Although we considered only point forecasts (conditional mean) in this paper, we recognize that range forecasting is another important and meaningful direction. For example, researchers can use the 95% confidence interval as the forecast range and examine the percentage of realized data falling in that range. We leave this possibility to future study.

Notes

1 According to Porter (1998), an industrial cluster comprises upstream industries, downstream industries, and peripheral industries in a production chain that spans from materials to final products.

2 We recognize that our variable selection could be somewhat subjective. An alternative and objective approach to search for endogenous variables is using the Granger's causality (e.g., Hsu et al., 2003). However, we did not want to include less explanatory variables, especially in a Bayesian structure.

3 Logarithmic transformation can be found in Kadiyala and Karlsson (1997) and Simpson et al. (2001). Preliminary deseasonalization can also be found in Doan, Litterman, and Sims (1984), Kumar et al. (1995), Dua and Ray (1995), Ravishanker and Ray (1997), Salazar and Weale (1999), Marchetti and Parigi (2000), and Simpson et al. (2001).

4 There are two kinds of multi-step ahead forecasting, the static one and dynamic one. In static forecasting, people use parameters estimated based on t-1 to t-w, but put actual data t 1 to t-s-1 into the model for advanced forecasts (t 2 thru t s).

5 In Hsu et al. (2003), the LBVAR models do perform better in Theil U in their empirical study of 1998-2000. We attribute the bad Theil U performance of LBVAR forecasts to the Internet Bubble Burst in 2001-2002 and the recession in the information technology markets since 2000. Both events make the prediction job more difficult.

6 Comparing the proposed model with other industrial surveys and forecasting reports was also found in Litterman (1986), Mills and Pepper (1999), Marchetti and Parigi (2000). However, their studies were dealing with economic indicators, and ours is about industrial production of specific industries.

7 ITRI has played an important role in developing Taiwan's semiconductor industry as noted previously. ITRI is also a leading institute in providing market information of technology industries.