Neural Network Analysis in Forecasting the Malaysian GDP

• 발행기관: 한국유통과학회
• 저자명: Nur Azura SANUSI Adzie Faraha MOOSIN Suhal KUSAIRI
• 간행물 정보: 『The Journal of Asian Finance, Economics and Business(JAFEB)』Vol. 7 No.12, 109~114쪽, 전체 6쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2020.12.30

영문 초록

The aim of this study is to develop basic artificial neural network models in forecasting the in-sample gross domestic product (GDP) of Malaysia. GDP is one of the main indicators in presenting the macro economic condition of a country as set by the world authority bodies such as the World Bank. Hence, this study uses an artificial neural network-based approach to make predictions concerning the economic growth of Malaysia. This method has been proposed due to its ability to overcome multicollinearity among variables, as well as the ability to cope with non-linear problems in Malaysia’s growth data. The selected inputs and outputs are based on the previous literatures as well as the economic growth theory. Therefore, the selected inputs are exports, imports, private consumption, government expenditure, consumer price index (CPI), inflation rate, foreign direct investment (FDI) and money supply, which includes M1 and M2. Whilst, the output is real gross domestic product growth rate. The results of this study showed that the neural network method gives the smallest value of mean error which is 0.81 percent with a total difference of 0.70 percent. This implies that the neural network model is appropriate and is a relevant method in forecasting the economic growth of Malaysia.

목차

1. Introduction

2. Literature Review

3. Research Methods

4. Results and Discussion

5. Conclusion

References

키워드

Economic Growth Developed Nation Forecasting Neural Network NARX Model