Modelling Inflation Rates Provinces in Indonesia Period 2013-2017 with Spatial Durbin Model (SDM) Dynamic Using Spatially Corrected Blundell-Bond (SCBB)

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 ( Inf t-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 ( W Inf t ), the inflation rate in the neighborhood provinces at the previous time ( W Inf t-1 ), economic growth ( PE t ), SBI interest rates ( SBI t ), SBI interest rates in neighborhood provinces ( W SBI t ), Consumer Price index ( CPI ) set by the government in that province ( CPI t ) and neighborhood provinces ( WCPI t ).