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This study analyzes the dynamics of Non-Performing Loans (NPLs) in the Indonesian banking sector by examining both internal and external factors affecting financial stability. The variables included in the research are NPL, Loan to Deposit Ratio (LDR), lending interest rate, inflation, Household Debt to Income (HDTI), fintech lending, and Capital Adequacy Ratio (CAR). Using annual secondary data from 2005 to 2024, sourced from the World Bank and Statistics Indonesia (BPS), the study employs a Vector Autoregression (VAR) method. This method includes stationarity tests, optimal lag selection, cointegration tests, Impulse Response Function (IRF), and Forecast Error Variance Decomposition (FEVD). The results show that most variables demonstrate a dominant contribution from their own shocks, although interactions between variables remain significant. The IRF analysis reveals that CAR and HDTI are relatively stable and quickly return to equilibrium, while fintech lending, inflation, and NPLs show more volatile responses, making them more susceptible to external shocks. LDR and lending interest rates are sensitive in the short term but tend to stabilize over the long run. FEVD further indicates that inflation plays a significant role in driving NPL variations, while fintech lending is closely associated with CAR in the long term. The study concludes that the stability of Indonesia’s banking sector is influenced by both internal factors like CAR and LDR, as well as external factors such as inflation, fintech lending, and household debt. Thus, a coordinated approach involving monetary policy, macroprudential measures, and financial supervision is crucial to enhance the resilience of the banking sector against global and domestic economic shifts.

The digital economy has significantly transformed economic growth by introducing innovations in payment systems and financial services. The modernization of payment instruments through monetary policy has enhanced the ability to control inflation and ensure financial system stability. This study aims to analyze the effectiveness of monetary policy and the utilization of the digital economy in maintaining financial stability in Indonesia. Using time series data from 2010 to 2024 obtained from the World Bank, this research applies the Vector Autoregression (VAR) method to examine both short-term and long-term relationships among variables, including e-money, money supply, inflation, exchange rate, interest rate, and credit card usage. The results show that e-money has a significant reciprocal influence on the money supply, while inflation is also affected by e-money and interest rates. The impulse response function demonstrates that the interactions among these variables tend to converge towards equilibrium over time. Variance decomposition analysis indicates that in the short term, e-money primarily drives financial stability, whereas in the medium and long term, the money supply plays a dominant role. Overall, the findings suggest that monetary policy, supported by digital economic systems, effectively enhances financial system stability in Indonesia. This research contributes to understanding the dual effect of digital payment innovations and provides recommendations for policymakers to strengthen financial inclusion, economic resilience, and macro-financial stability in the digital era.

This study investigates the monetary transmission mechanisms influencing inflation and exchange rates across seven Southeast Asian countries (Myanmar, the Philippines, Indonesia, Malaysia, Singapore, Thailand, and Vietnam) over the period 2010–2023, with special focus on the impact of the COVID-19 pandemic. The research addresses the problem of macroeconomic instability, particularly the volatility in inflation and currency values during crisis periods, and aims to identify the dominant monetary factors affecting these indicators. The study employs a mixed quantitative approach using Structural Vector Autoregression (SVAR), Panel Autoregressive Distributed Lag (ARDL), and Paired Sample t-Test to analyze the short-term and long-term relationships among key variables: Gross Domestic Product (GDP), investment, money supply (M2), interest rates, inflation, and exchange rates. Findings reveal that GDP is the most influential factor impacting both inflation and exchange rates, followed by money supply and interest rates. The variance decomposition analysis confirms that these monetary variables significantly explain macroeconomic fluctuations in both pre- and post-pandemic contexts. The t-Test further indicates statistically significant changes in inflation and exchange rates before and after the pandemic, highlighting the disruptive effect of COVID-19 on economic stability. The results demonstrate that inflation declined significantly in most countries during the pandemic, while exchange rate behavior varied depending on economic resilience and policy responsiveness. The study concludes that maintaining macroeconomic stability requires not only monetary policy coordination but also effective public health crisis management. This research contributes to the regional policy discourse by offering empirical insights and evidence-based recommendations to strengthen economic resilience in Southeast Asia.

This study explores employment development strategies aimed at bolstering economic growth in North Sumatra Province using the Vector Autoregression (VAR) model and an eighteen-year time series dataset. The variables analyzed include the Human Development Index (HDI), total population, Gross Regional Domestic Product (GRDP), Labor Force Participation Rate (LFPR), and Open Unemployment Rate (OUR). The estimation results reveal dynamic interrelationships among these variables over short, medium, and long-term periods. The VAR analysis with a lag of 2 illustrates how each variable contributes to both itself and the other variables. It also shows that past variables (t-1) significantly impact current variables. Furthermore, the response function analysis identifies how a change in one variable is responded to by others across different time horizons. Stability analysis confirms that all variables maintain medium-to-long-term stability over a five-year period. The Forecast Error Variance Decomposition (FEVD) highlights HDI, population, and GRDP as the most influential variables in shaping the employment system and economic development overall. The VAR model used meets the stability test criteria, making the findings a reliable basis for policy research.

This study aims to analyze the dynamic relationship between population density, Gross Regional Domestic Product (GRDP) per capita, and regional economic inequality in Indonesia over the period 1995–2024. Regional inequality is measured using the Gini Ratio as a key indicator. A quantitative method with a time series approach is employed using the Vector Autoregression (VAR) model. The analysis includes unit root testing, optimal lag selection, VAR estimation, impulse response function (IRF), variance decomposition, and Granger causality testing. The results show that population density has a positive effect on regional inequality, while GRDP per capita has a negative effect. However, both variables are statistically insignificant. The impulse response analysis indicates that a shock in population density tends to increase inequality in the short term, whereas a shock in GRDP per capita tends to reduce inequality. The Granger causality test reveals that population density regional inequality, while GRDP per capita does not have a significant causal effect. Overall, the findings suggest the importance of equitable economic development and population control policies in reducing regional disparities in Indonesia.

This study aims to analyze the variable contribution of the interaction of monetary policy variables (COURSE, GDP, INFLATION, CONSUMPTION and INTEREST). This study uses secondary data or time series from the first quarter of 2014 to the first quarter of 2024. The data analysis model in this study is the Structural Vector Autoregression (SVAR) model and sharpened with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analysis. The results of the SVAR analysis show that the past variable (t-1) contributes to the current variable both to itself and other variables and from the estimation results it turns out that there is a reciprocal relationship between variables where all variables, namely monetary policy variables (GDP, INFLATION, CURRENCY, CONSUMPTION and INTEREST) contribute to each other.