VECM and Bayesian VECM for Overparameterization Problem

Abstract Overparameterization is the amount of data used in the study is less than the number of estimated parameters. Overparameterization problems can cause the forecasting ability to be weak because the model is not suitable. This problem often occurs in complex models such as Vector Error Correction Model (VECM). This study discusses VECM and Bayesian VECM (BVECM), which aims to analyze the relationship between macroeconomic variables in Indonesia. First, estimate parameters of VECM with Maximum Likelihood Estimation. Second, estimate parameters of VECM with a Bayesian approach (BVECM). 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 amount of data in this study is less than the number of estimated parameters that causing overparameterization problems. Based on literature, Bayesian method can avoid overparameterization problems which can not be overcome by Maximum Likelihood Estimation. The model obtained from this study is the VECM(3) and BVECM(3). In the VECM analysis, the residuals did not meet the assumptions of diagnostic model. However, diagnostics of BVECM models show that it has been proven that the model is suitable. This conclusion is relevant to the statement that the Bayesian method can solve the problem of overparameterization.