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Modelling Inflation Rates Provinces in Indonesia Period 2013-2017 with Spatial Durbin Model (SDM) Dynamic Using Spatially Corrected Blundell-Bond (SCBB) Widya Reza; Henny Pramoedyo; Rahma Fitriani
Wacana Journal of Social and Humanity Studies Vol. 24 No. 4 (2021)
Publisher : Sekolah Pascasarjana Universitas Brawijaya

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Abstract

Inflation control is one of the main macroeconomic problems that must be solved in Indonesia. In inflation control, it is necessary to do an analysis to determine the factors that influence it. The Phillips Curve theory states that one of the factors that influence inflation in a given period is the inflation of the previous period so that dynamic relationships apply that require dynamic modeling. In a dynamic model, the lag of the response variable as a predictor variable causes endogeneity problems so that a parameter estimation method is needed to overcome it. In addition to inflation in the previous period, factors that are thought to influence the inflation rate in Indonesia are economic growth, real interest rates, money supply, and the Consumer price index (CPI) set by the government. The closeness between provinces in Indonesia can cause the inflation rate of a province to be similar to the inflation rate of other provinces through the transfer of information and knowledge, causing spatial dependence. Spatial dependence can occur in a response and predictor variables called Spatial Durbin Model (SDM). To overcome this problem a method is needed to overcome the problem of endogeneity and spatial dependence, namely Spatially Corrected Blundell-Bond (SCBB). The results showed that the inflation rate in the previous year (Inft-1) had a significant influence on the inflation rate this year. In addition, another variable that significantly influences the inflation rate is the inflation rate in neighborhood provinces (WInft), the inflation rate in the neighborhood provinces at the previous time (WInft-1), economic growth (PEt), SBI interest rates (SBIt), SBI interest rates in neighborhood provinces (WSBIt), Consumer Price index (CPI) set by the government in that province (CPIt) and neighborhood provinces (WCPIt).
Estimating Gross Regional Domestic Product (GRDP) District/City in East Nusa Tenggara with Spatial Dynamic Panel Data Febrya Christin Handayani Buan; Rahma Fitriani; Nurjannah Nurjannah
Wacana Journal of Social and Humanity Studies Vol. 24 No. 4 (2021)
Publisher : Sekolah Pascasarjana Universitas Brawijaya

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Abstract

East Nusa Tenggara Province has the lowest Gross Regional Domestic Product (GRDP) value in Indonesia. The local governments try to increase the value of GRDP by providing capital for productive businesses, increasing human resources, and managing the economic sector in each district/city. The GRDP value is influenced by dynamic economic factors. In a functional region, GRDP values in a region are correlated with neighboring regions. Regions that have homogeneous characteristics will tend to have the same economic condition, therefore indicating there is spatial dependence. Therefore, to know the pattern of GRDP value should be doing periode observed use panel data. Model panel data that can accommodate the effect of each factor on GRDP value has dynamic and dependence spatial, the model can be more useful to capture them. This study will apply the model to the cast of GRDP value in the district/city of East Nusa Tenggara Province with economic factors affecting GRDP value is labor (L), population (P), investment (Inv), and locally generated revenue (LGR). The results of variable estimated the found that significantly affect the GRDP values are investment (Inv) and locally generated revenue (LGR), furthermore GRDP value affect by GRDP value in the previous period and GRDP value by neighbor regions.