Forecasting macroeconomic variables is crucial to measure dynamic changes during uncertain economic conditions. This study examines and analyzes the appropriate and accurate forecasting model to predict macroeconomic variables in Maluku Province. The main variables used are economic growth, unemployment, inflation, and poverty. The modeling used in this study were Bayesian Vector Autoregressions Model and the Univariate Benchmark Model. The results of this study indicate that the two models have different specifications and forecasting directions. The value of the Univariate Benchmark model’s forecast error size is relatively smaller than that of the Bayesian Vector Autoregressions Model. The results of forecasting macroeconomic variables in Maluku Province have a relatively good level of accuracy and are close to the actual value of the sample period. The Error Correction Model test results show that only the Error Correction Term variable significantly affects the poverty level in the short term. Meanwhile, in the long term, the unemployment rate has a significant effect, and the model used is proven valid. The forecasting results from the model show that the Maluku provincial government must maintain the stability of macroeconomic variables, especially the inflation rate and unemployment rate, because they tend to increase in the coming year. It can have an impact on reducing people’s purchasing power.
Inequality of income distribution and its determinants still leaves mixed debate and empirical findings. The source of inequality in population income distribution is in Eastern Indonesia (KTI) in line with the economic development performance that is still low compared to Western Indonesia (KBI). This study aims to examine the socio-economic factors that influence the income distribution gap in KTI, with a focus on economic growth, employment opportunities, farmer exchange rates, regional minimum wages, and education. Quantitative analysis tools used are panel data regression models with fixed effect model estimation techniques. The data used is panel data of 12 provinces in Eastern Indonesia, for the period 2010-2017. The results showed that both partially and simultaneously economic growth, employment opportunities, farmer exchange rates, and education levels contributed significantly to reducing the income distribution gap. The recommendation for regional governments is to accelerate economic growth accompanied by the expansion of employment opportunities and education development equally, and also the optimization of agricultural sector development.
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