Abstract
The use of the BI 7 days repo rate as a new benchmark rate replacing the BI rate, raises hopes that the transmission of monetary policy in Indonesia can run faster and more effectively. This study uses monthly time series data for the period August 2016-December 2018, using the Vector Autoregression (VAR) analysis. The results show that the transmission of monetary policy through the interest rate and asset price channels has been effective, while the transmission of monetary policy through the exchange rate channel has not been effective in driving economic growth in Indonesia. The decline in the benchmark interest rate can be responded quickly by interbank money market interest rates, banking interest rates, stock prices, and output. The exchange rate can also respond quickly to changes in interest rates, but changes in the exchange rate have not been able to influence net exports and increase output.
Highlights
Mekanisme transmisi kebijakan moneter merupakan topik yang selalu dibahas dalam ekonomi moneter baik secara teori maupun praktiknya di bank sentral
The results show that the transmission of monetary policy through the interest rate and asset price channels has been effective, while the transmission of monetary policy through the exchange rate channel has not been effective in driving economic growth in Indonesia
Trace test indicates 3 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Summary
Di mana PE adalah pertumbuhan ekonomi, BI7D adalah BI 7 Days Repo Rate, dan IR adalah suku bunga pasar uang (RPUAB) dan suku bunga perbankan (suku bunga deposito dinotasikan dengan RDEPO dan suku bunga kredit dinotasikan dengan RKREDIT). Berdasarkan hasil uji Augmented Dickey-Fuller (ADF) pada menunjukkan bahwa hanya satu variabel yang lolos pada uji stasioner pada tingkat level, yaitu suku bunga kredit (RKREDIT). Sehingga dilanjutkan dengan uji stasioner pada derajat satu (first difference). Hasil menunjukkan bahwa seluruh variabel stasioner pada tingkat signifikansi 5 persen dan 10 persen (Tabel 2). Berdasarkan hasil uji pada Tabel 3, lag optimum 1 hanya memenuhi satu kriteria yaitu Schwarz Criterion (SC), sedangkan pada lag optimum 2 terdapat empat kriteria yang memenuhi yaitu Likelihood Ratio (LR), Final Prediction Erorr (FPE), Akaike Information Criterion (AIC) dan Hannan Quinn (HQ). Penelitian ini menggunakan lag optimum 2 dengan metode Akaike Information Criterion (AIC). Hasil uji kointegrasi menggunakan metode Johansen pada Tabel 4 menunjukkan bahwa paling tidak terdapat tiga kombinasi linier dari variabel-variabel yang terdapat dalam model tersebut memiliki hubungan kointegrasi (jangka panjang)
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