Sustainable development is one of the goals of ASEAN as a UN cooperation partner. The high level of CO2 emissions as one indicator of the decline in environmental quality in several ASEAN member countries, requiring an appropriate policy in order to achieve sustainable development targets. Based on previous research, the relationship between CO2 emissions, income, and health expenditure is not only simultaneous but also dynamic (changes in a variable spread to the current period and future periods). The relationship between these variables can be described in a system of dynamic simultaneous equations. The ten ASEAN country panel data for eight years (2008-2015) will be used by applying a simultaneous dynamic data panel model. GMM-System Estimator and GMM Arellano-Bond are used to estimate the parameters of dynamic models. Based on the accuracy of the sign with economic theory as well as from the standard error estimator produced, estimates from the GMM-System Estimator are considered better. The resulting estimates indicate a significant simultaneous influence between health expenditure and per capita income and between health expenditure and CO2 emissions. Per capita income growth affects the growth of CO2 emissions indirectly through per capita health expenditure. In addition, the lag of each variable, namely per capita income, per capita health expenditure, and CO2 emission has a positive and significant effect that indicates a long-run multiplier effect.
The Consumer Price Index (CPI), stock prices and the rupiah exchange rate to the US dollar are important macroeconomic variables which their movements show the economic performance and can affect the monetary and fiscal policies of Indonesia. This makes forecasting effort of these variables become important for policy planning. While many previous studies only focus on examining the effect among macroeconomic variables, this study uses ARIMA (univariate method), transfer function and VAR (multivariate methods) to measure the forecasting accuracy and also observing the effect between these macroeconomic variables. The results showed that the multivariate methods gave better explanation about the relationship between variables than the simple one. Otherwise, the results of accuracy comparison showed that the multivariate methods did not always yield better forecast than the simple one, and these conditions in line with the results and conclusions of M3 and M4 competition.
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