Share price as one kind of financial data is the time series data that indicates the level of fluctuations and heterogeneous variances called heteroscedasticity. The method that can be used to overcome the effect of autoregressive conditional heteroscedasticity effect is the generalised form of ARCH (GARCH) model. This study aims to design the best model that can estimate the parameters, predict share price based on the best model and show its volatility. In addition, this paper discusses the prediction-based investment decision model. The findings indicate that the best model corresponding to the data is AR(4)-GARCH(1,1). The model is implemented to forecast the stock prices of Indika Energy Tbk, Indonesia, for 40 days and significantly presented good findings with an error percentage below the mean absolute.
Fiscal decentralization is an effort to reform governance so that it has a more effective and efficient structure so that it can improve services to the community. Efforts to achieve these goals are largely determined by the availability of human resources, natural resources, and other economic potential. The formation of New Autonomous Regions (NAR) grew rapidly, but on the other hand local governments were unable to fund development activities independently but were dependent on balance funds. The objective to be achieved is to analyze the effect of regional government spending on education, health, and infrastructure, as well as other variables namely labor on the economic growth of new autonomous regions in Indonesia. The analysis model used is panel data regression. The results of the study prove that local government spending in real per capita education, real health (lag-1) per capita, and real per capita infrastructure, and the number of workers have a positive and significant effect on economic growth. Economic growth that occurs in the district is not different from the city, so also in the base sector is mostly no different except the mining and quarrying sector.
Future natural gas (FNG) price is a collected data over the years and is a volatile movement in the market. In other words, FNG price is categorised as a time series data with volatility in both variance and mean, as well as most likely in some cases having heteroscedasticity problem. To come up with an estimated prediction model, some analysis tools, such as autoregressive integrated moving average (ARIMA) and generalised autoregressive conditional heteroscedasticity (GARCH), are introduced to find the best-fitted model having the smallest error value with high significance of probability value. This study aims to examine the best-fitted model that allows us to forecast FNG prices more accurately in the near future. There are 2842 observed data of daily FNG prices from 2009 to 2019 as the input of study objects. The finding suggests that the first measurement model of ARIMA (1,1,1) does not fit the model as having a non-significant probability value. Thus, it is required to check its heteroscedasticity by conducting an ARCH effect test. It is concluded that a data set has an effect of ARCH, so AR (p)-GARCH (p,q) model is then tested. AR (1)-GARCH (1,1) model is believed to be a best-fitted model having a significant P < 0.0001 with significantly small mean squared error and root mean squared error values, indicating that it has a very accurate prediction model. The forecasting model is to adjust the offered recommendation of policy for the government regarding the issue of high volatility of daily FNG prices in the future. We then offer a best-suited policy for some certain governments like Indonesia to give subsidy for targeted users in order to keep increasing their use of FNG that will expectedly affect their marketable product innovation and expansion, so economic growth in Indonesia is maintained.
Coal is a mineral fuel commodity considered important as a source ofenergy and is traded among countries. Indonesia is one of the largest coal producing countries in the world. This study aimed to analyse the relationship between the net export volume, GDP per capita of destination countries, real exchange rate, and Indonesian coal export prices. The existence of a causal relationship between exports and economic growth shows that there is a relationship between net exports and future economic growth. Economic growth is an increase in people's per capita income without paying attention to changes in the economic structure.The study uses panel data of 5 biggest coal trading partner countries of Indonesia during the period 2015-2019, by using the dynamic panel analysis method, where a dependent variable is not only determined by the value of independent variables at the research period, but is also determined by the value of previous period. The dynamic panel method is characterized by the lag of the dependent variable which is correlated with the residual among the independent variables. The dynamic panel data regression method can be used to determine the short-term effect,and the long-term effect as well.Based on the estimation results of the Generalized Method of Moment (GMM) Arellano Bond, in the study period the exchange rate and export prices had a significant negative effect on the volume of Indonesian coal exports. GDP per capita has no significant effect on the volume of Indonesia's coal exports.Furthermore, the short-term elasticity approach for the exchange rate is -0.029159 and for the long term is 0.3616521. These results indicate that the calculation of the short-term and long-term elasticity of the exchange rate (ER) is inelastic and negative with different magnitudes. In addition, it explains that in the short term an increase in the exchange rate of 1 percent will reduce net exports in the short term by 2.9 percent.
The study of multivariate time series data analysis has become many topics of research in the fields of economics and business. In the present study, we will analyze data energy inflation and gasoline prices of Indonesia over the years from 2014 to 2020. The purpose of this study is to obtain the best model of the dynamic relationship between inflation and gasoline prices. The dynamic modeling that will be used in this research is modeling using the Vector Autoregressive (VAR) model. From the analysis results, the best model is the VAR model with order 3 (p=3), VAR(3). Based on the best model, VAR(3), further studies will be discussed with regard to Granger causality analysis, Impulse Response Function, and Forecasting.
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