1 |
Tariq, R., Khan, M. A., & Rahman, A. (2020). How does financial development impact economic growth in Pakistan?: New evidence from threshold model. Journal of Asian Finance, Economics and Business, 7(8), 161-173. https://doi.org/10.13106/jafeb.2020.vol7.no8.161
DOI
|
2 |
Tkacz, G. (2001). Neural network foreasting of Canadian GDP growth. International Journal of Forecasting, 17, 57-69.
DOI
|
3 |
World Bank. (2012). Retrieved February 15, 2012, from http://data.worldbank.org/indicator
|
4 |
Zemouri, R., Gouriveau, R., & Zerhouni, N. (2010). Defining and applying prediction performance metrics on a recurrent NARX time series model. Neurocomputing, 73(13-15), August, 2506-2521.
DOI
|
5 |
Zhang, G., Hu, Y. M., Patuwo, E. D., & Indro, C. D. (1999). Artificial neural network in bankcruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116, 16-32.
DOI
|
6 |
Baily, D., & Thompson, D. M. (1990). Developing neural network applications. AI Expert, 12, 33-41.
|
7 |
Celik, A. E., & Karatepe, Y. (2007). Evaluating and forecasting banking crises through neural network models: An application for Turkish banking sector. Expert Systems with Applications, 33, 809-815.
DOI
|
8 |
Co, C. H., & Boosarawongse, R. (2007). Forecasting Thailand's rice export: Statistical techniques vs. artificial neural networks. Computers & Industrial Engineering, 53, 610-627.
DOI
|
9 |
Dinh, D. V. (2020). Impulse response of inflation to economic growth dynamics: VAR model analysis. Journal of Asian Finance, Economics and Business, 7(9), 219-228. https://doi.org/10.13106/jafeb.2020.vol7.no9.219
DOI
|
10 |
Economic Planning Unit (EPU). (2001). Outlined Perspective Plan III. Kuala Lumpur: Malaysia's National Printer.
|
11 |
Economic Planning Unit (EPU). (2010). First Guideline of 10th Malaysia Plan. Retrieved January 24, 2018, from https://www.epu.gov.my/en/resources/guidelines-and-procedures/firstguideline-10th-malaysia-plan
|
12 |
Economic Planning Unit (EPU). (2011). 10th Malaysia Plan 2011-2015. Retrieved January 24, 2018, from https://www.pmo.gov.my/dokumenattached/RMK/RMK10_E.pdf
|
13 |
First Malaysia Plan. Prime Minister Office, (2018). Retrieved on January 24, 2018, from http://www.pmo.gov.my/dokumenattached/RMK/RMK1.pdf
|
14 |
Ismail, Z., & Khamis. A. (2003). Neural network in the prediction of palm oil prices. Journal of Technology, 39, 17-28.
|
15 |
Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10, 215-236.
DOI
|
16 |
Nguyen, L. P., & Pham, V. H. T. (2020). Trade of ICT products, government, and economic growth: Evidence from East AsiaPacific region. Journal of Asian Finance, Economics and Business, 7(8), 175-183. https://doi.org/10.13106/jafeb.2020.vol7.no8.175
DOI
|
17 |
Liliana, & Napitupulu, T. A. (2012). Artificial neural network application in gross domestic product forecasting an Indonesia case. Journal of Theoretical and Applied Information Technology, 45(2), 410-415.
|
18 |
Menezes Jr, J. M. P., & Barreto, G. A. (2008). Long-term time series prediction with the NARX network: An empirical evaluation. Neurocomputing, 71(16-18), October, 3335-3343.
DOI
|
19 |
National Economic Advisory Council. (2011). An Executive Summary of New Economic Model of Malaysia. Kuala Lumpur: Malaysia's National Printer.
|
20 |
Ninth Malaysia Plan. Prime Minister Office, (2018). Retrieved January 24, 2018, from http://www.pmo.gov.my/dokumenattached/RMK/RM9_E.pdf
|
21 |
Roldao Cancela, A. M. (2008). Comparative study of artificial neural network and Box-Jenkins ARIMA for stock prices indexes. Retrieved February 1, 2018, from https://repositorio.iscteiul.pt/bitstream/10071/1472/1/comparative%20study%20of%20artificial%20neural%20network%20and%20boxjenkins%20arima%20for%20stock%20price%20indexes.pdf
|
22 |
Sixth Malaysia Plan. Prime Minister Office, (2018). Retrieved January 24, 2018, from http://www.pmo.gov.my/dokumenattached/RMK/RM6.pdf
|
23 |
Lam, M. (2004). Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis. Decision Support Systems, 37, 567-581.
DOI
|