Abstract
This study uses Bayesian approach to estimate Vector Error Correction Model (VECM). The aims of this study is to analyze the relationship between macroeconomic variables in Indonesia. To analyze the best method to influence government targets or policies on economic growth by studying the relationships of many macroeconomic variables. Previous studies in analyzing the relationship between macroeconomic variables with VECM analysis, using the Maximum Likelihood Estimation. However this estimation method cannot solve the problem of overparameterization in VECM model. The variables used in this study are six macroeconomic variables in Indonesia in 2010 quarter 1 to 2019 quarter 4 are GDP, the money supply, exchange rate of rupiah to US dollar, exports, imports and interest rates. The number of data in this study is less than the number of estimated parameters causing overparameterization problems. Therefore, this study uses the Bayesian parameter estimation method to avoid overparameterization problems in economic data. The model obtained from this study is the BVECM(3) and it has been proven that the model is suitable (the model diagnostic test). Based on the parameter estimation results of BVECM(3), the significant variables affecting GDP are GDP itself, the money supply, exchange rate of rupiah to US Dollar, exports, imports and the interest rate for Bank Indonesia Certificates. In addition, there is a two-way relationship that affects each other, namely the relationship between GDP and the money supply, exports and imports, exports and interest rates, and between imports and interest rates.
Highlights
In the field of economics, multivariate time series analysis is commonly used, one of which is the Vector Error Correction Model (VECM) or restricted VAR model developed by Johansen and Julius (Enders, 2015)
The variables used in this study are six macroeconomic variables in Indonesia in 2010 quarter 1 to 2019 quarter 4 are Gross Domestic Product (GDP), the money supply, exchange rate of rupiah to US dollar, exports, imports and interest rates
The model obtained from this study is the Bayesian Vector Error Correction Model (BVECM)(3) and it has been proven that the model is suitable
Summary
In the field of economics, multivariate time series analysis is commonly used, one of which is the Vector Error Correction Model (VECM) or restricted VAR model developed by Johansen and Julius (Enders, 2015). The most commonly used parameter estimation techniques for VECM are Least Square and Maximum Likelihood Estimation. These methods have the advantage that they are easier to apply for a large number of observations. In VAR and VECM models, problems often occur, when there are too many parameters to be estimated and the number of data is less than the estimated parameters (overparameterization), the forecasting capacity is weak because the model is not suitable. The Bayesian parameter estimation method in the VAR model is to avoid the problem of overparameterization in economic data Tahir (2014)
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