• Muhammad Rismawan Ridha Badan Pusat Statistik



ECM, Inflation, Stability of Financial System


The current condition of economic openness is both an opportunity and a challenge that must be faced wisely by the government. Liberalization and economic integration will have an impact on financial market liberalization, which is highly vulnerable to create crisis in a banking system. This study aims to analyze the factors that influence the stability of the financial system in Indonesia by using the Error Correction Model (ECM). The variables used in this research is Capital Banking Credit sourced from Statistics Indonesia (BPS) and Exchange Rate, Inflation, and Money Supply sourced from the International Monetary Fund (IMF) between 2010 and 2015. The results of the study show that; 1) ECT coefficient which has negative and significant value explains that the model is valid. 2) Inflation significantly affects the stability of the financial system in Indonesia in the long and short term


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Ali, Awdeh. 2017. The Determinant of Credit Growth in Lebanon. International Business Research. Vol. 10, No. 2

Auel, M.& Helder, F, M. 2011. Macroeconomic relevance of credit channels: Evidence from an emerging economy under inflation targeting. Journal of Economic Modelling. 28 (3): 965-979

Korkmaz, S. 2015. Impact of Bank Credits on Economic Growth and Inflation. Journal of Applied Finance and Banking. Vol. 5, No. 1. Sciencepress Ltd.

Kasmir. 2010. Manajemen Perbankan. Depok: RajaGrafindo Persada Press.

Mankiew, N. G. 2006. Macroeconomics. New York: Worth Publisher Press.

Mariusz, P. & Katarzyna, W. 2016. The impact of the financial system on economic growth in the context of the global crisis: empirical evidence for the EU and OECD countries. Empirica. Vol. 44, 10.1007/s10663-016-9323-9

Matthew, L. & Kraft, P. 2017. The General Error Correction Model in Practice. Research and Politics. 4. 205316801771305. 10.1177/2053168017713059.

Mekki, H & Maktouf, S. 2018. Overall effects of financial liberalization: financial crisis versus economic growth. Journal Of International Review of Applied Economics. 1-28. 10.1080/02692171.2018.1515898.

Negara, H. R. P., Tamur, M., Syaharuddin, Apandi, T. H., Kusuma, J. W., & Hamidah. (2020). Computational modeling of ARIMA-based G-MFS methods: Long-term forecasting of increasing population. International Journal of Emerging Trends in Engineering Research, 8(7), 3665–3669.

Patcharee, P., et al. 2018. Economic integration in the Asean and its effect on empirical economic growth. Journal Of Applied Economic Sciences. Vol. 13, 922-935.

Robert, Kissel. & Poserina, James. 2017. Optimal Sports Math, Statistics, and Fantasy. Newyork: Academic Press.

Rosadi, Dedi. 2016. Analisis Runtun Waktu dan Aplikasinya dengan R. Yogyakarta:. Gadjah Mada University Press.

Ruiz E, M. 2019. An Introduction to The Hybrid Economics Models. 10.13140/RG.2.2.23045.29920/1.

Smith, Jeremy and Otero, J. (2000) Testing for cointegration : power versus frequency of observation - further Monte Carlo results. Economics Letters, Volume 67 (Number 1). pp. 5-9. Warwick University doi:10.1016/S0165-1765(99)00245-1

Tursoy, T. 2017. Causality between Stock Prices and Exchange Rates in Turkey: Empirical Evidence from the ARDL Bound Test and a Combined Cointegration Approach. Journal Of Financial Studies. 5,8. 10.3390/ijfs5010008