Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/35256
Title: Forecasting Singapore’s pharmaceutical industry using time series models.
Authors: Lee, Kai Wee.
Simandjuntak, Daniel Perdana.
Zhuo, Yaohong.
Keywords: DRNTU::Social sciences::Economic development::Singapore
DRNTU::Social sciences::Economic theory::Macroeconomics
Issue Date: 2010
Abstract: In this study on Singapore’s pharmaceutical industry, our objective is to investigate the feasibility of using time series methods to generate one-year forecast for the industrial output based on 204 observations from January 1992 until December 2008. This study is motivated by the lack of published studies on forecasting for the pharmaceutical industry output in Singapore. Time series methods such as the Box-Jenkins and GARCH (General Autoregressive Conditional Heteroscedasticity) methodology were used. In-sample and out-of-sample forecasts were generated using the recursive estimation method and were evaluated based on the root mean square error (RMSE) of their forecasts. The results showed that the ARIMA (2,1,1) with its 1st autoregressive lag removed produced the best forecast using the Diebold-Mariano Statistic with root mean square error as its criterion. However, both of the time series models we have chosen were relatively inadequate in forecasting the pharmaceutical industry output in Singapore. We explained volatility in industrial output and our feedback came mostly from Singapore Economic Development Board and Bristol Meyers Squibb Singapore. Judgmental Forecasting and forecast combinations were suggested as alternative approaches. The report ended with limitations of our study and scope for further research. A regression approach may be feasible, if certain informational requirements can be satisfied.
URI: http://hdl.handle.net/10356/35256
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:HSS Student Reports (FYP/IA/PA/PI)

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