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.

The aim of this study is to find out the time-series nexus of public debt and economic growth in Malaysia. For an upper-middle income country, Malaysia had experienced over 50% ratio of debt to GDP since 2009 until now. The question arises is whether this trend is healthy to the economy. With a focus into the debt-to-GDP ratio from 1970-2015, this study investigates the short-run and long-run relationship between public debt and economic growth in Malaysia. This study used secondary data by collecting time-series data (1970-2015) from the World Bank Data and Bank Negara Malaysia. Autoregressive Distributed Lag (ARDL) model is applied in this study to examine the relationship between debt and economic growth. Based on ARDL framework, it shows that there is a long-run effect between the debt and economic growth in Malaysia. While the significance value of Error Correction Term shows that there is a long-run adjustment in the short run. Generally, this study found government expenditures, in the long run, strongly influence the GDP per capita. Through the findings, the government expenditures could increase the GDP per capita. The study also reveals that any increment of the debt ratio will result in reduction of the GDP per capita.

Financial literacy is one of the sustainable development goals of huge concern of governments. Governments explore solutions addressing policies to improve financial literacy. Nevertheless, financial management has such a broad scope and is not just limited to knowledge. As human nature, individuals are born with different confidence levels that include various financial abilities. This study aims to investigate the household-financial efficacy through the application of psychometric instruments, risk preference, and demographic characteristics toward consumption decision behavior. The research is based on a survey 479 households in the peninsular Malaysia, and utilizes the structural equation model, cluster proportional and systematic random sampling, and two measurements – composite reliability and average variance extracted. Results show that households’ financial efficacy is one of the critical factors that explain the households’ consumption decision behavior. Also, risk preference, gender and area location (rural or urban) of the household determined the consumption decision behavior of the household. The effectiveness of consumption decision is not only determined by financial literacy, but also financial efficacy. The implications of this paper may help to design policies in narrowing the broad gap between the rural and urban level of financial efficacy. The government needs to take appropriate actions to fix it.