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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.

This study aims to determine the effect of fintech, the amount of money in circulation, interest rates and economic growth on the analysis of digital economic trends on monetary policy in Indonesia. There are four variables in this study, namely fintech, the amount of money in circulation, interest rates and economic growth. The analysis method used is Vector Autoregression with the Impluse Response Function test or abbreviated as IRF and the Forecast Error Variance Decomposition test commonly abbreviated as FEVD, stationarity test, cointegration test, lag structure stability test and optimal lag length test. There is a contribution from each variable to the variable itself and other variables, according to the results of the Vector Autoregression study with a lag basis of 2. In addition, the results of the Vector Autoregression analysis show that the past variable (t-1) contributes to the current variable both to the variable itself and to other variables. The results of the analysis show that there is a reciprocal relationship between the variables. By using response function analysis, we can see if there is a response from other variables to changes in one variable in the short, medium, or long term. In addition, we know that the stability of all variables is formed in the short, medium, and long term. According to the Variance Decomposition Analysis, factors such as Fintech and Money Supply contribute the most to the variable itself. However, other variables that have the greatest influence on the variable itself and are supported by other variables in the short, medium, and long term are economic growth and interest rates are most influenced by Fintech.

This study aims to determine the effect of monetary policy on economic growth. This study uses the Vector Autoregression method by completing the assumption test and estimation on the research variables of inflation, investment, credit, interest rates and gross regional domestic product. Time series research data from 2003-2022 sourced from the Central Bureau of Statistics of North Sumatra (BPS) SUMUT with the results obtained are inflation and interest rates give the results that inflation is influenced by investment and credit, until investment affects gross regional domestic product. Then credit also affects inflation and interest rates and gross regional domestic product is influenced by investment and credit. Thus, in determining policies that encourage economic growth the government must consider a more coordinated monetary policy strategy to deal with the dynamics of strengthening interest rates, controlling inflation, can be measures that support economic stability.

This study examines the influence of monetary policy, particularly interest rates, on economic stability in the TIMI countries (Thailand, India, Malaysia, and Indonesia). It underscores the vital role of interest rates in controlling inflation and stimulating economic growth. Utilizing a Vector Autoregression (VAR) model, the research analyzes the reciprocal relationships between crucial economic indicators such as GDP, CPI, exchange rates, consumption, interest rates, and trade balances from 2008 to 2022. For instance, adjustments in interest rates can influence investment levels, consumption patterns, and inflation rates, thereby affecting overall economic activity. The Granger causality tests indicate that short-term relationships between these variables are insignificant, but long-term interactions are evident. This supports the Johansen cointegration results, which confirm two cointegrated equations at the 5% significance level. The study emphasizes maintaining interest rate stability for sustainable economic growth and price stability. It highlights that fluctuations in interest rates, influenced by global economic conditions and domestic economic policies, play a crucial role in the economic performance of TIMI countries. Recommendations for central banCM include implementing responsive and adaptive interest rate policies to manage inflation, foster economic growth, and maintain exchange rate stability. This approach is essential for addressing disparities in income, education, healthcare, and technology access, which are critical for equitable economic development. In conclusion, this research underscores the importance of a nuanced understanding of monetary policy's impact on economic stability and the need for coordinated efforts between fiscal and monetary authorities to achieve long-term

This research aims to determine the effect of the government expenditure model and green growth based on green energy consumption in Indonesia which has 4 variables, namely carbon emissions, energy consumption, economic growth and government expenditure. The analytical method used in this research is the Vector Auto Regression (VAR) model with the Impulse Response Function (IRF) test, Forecast Error Varince Decomposition (FEVD), stationarity test, cointegration test, structural lag stability test, and optimal lag length test. . The results of the Vector Autoregression research using lag 1 as the basis show the contribution of each variable to the variable itself and other variables. The results of the Vector Autoregression analysis also show that the past variable (t-1) contributes to the current variable, both the variable itself and other variables. From the results of the analysis, there is a reciprocal relationship between one variable and another variable. Response Function Analysis shows the response of other variables to changes in a variable in the short, medium and long term, and it is known that the stability of the response of all variables is formed within a period of 5 years or the medium term. and long term. Variance Decomposition Analysis shows that there are variables that have the largest contribution to the variable itself in the short, medium and long term, such as CO2, EC, and GOV. Meanwhile, another variable that has the greatest influence on the variable itself in the short, medium and long term is CO2 which is strongly influenced by GOV and GDP.

This research to analyze the contribution of variables from three economic policies, with monetary policy through interest rate variables, exchange rates, and money supply in facing economic recession. Where the fiscal policy variable is through tax value. Then macroprudential policy through Non Performing Loan and Capital Adequacy Ratio variables. This study uses secondary data or time series, namely from December 2019 to February 2021. The data analysis model in this study is the Vector Autoregression (VAR) model which is seen from being sharpened with Impulse Response Function (IRF) analysis and Forecast Error Variance Decomposition (FEVD), Panel ARDL, and Different Tests. The results of the IRF analysis show that the stability of the response of all variables is formed in period 8 or the medium and long term, where the response of other variables to changes in one variable shows different variations, both from positive responses to negative responses or vice versa, and there are variables whose responses remain positive or remain negative from the short term to the long term. The results of the FEVD analysis show that for the short-term inflation variable it is influenced by inflation itself and in the medium and long term it is influenced by interest rates. For the JUB variable in the short term it is influenced by JUB itself and in the medium and long term it is influenced by NPL. For the interest rate variable in the short term it is influenced by JUB while in the medium and long term it is influenced by the exchange rate itself and CAR. For the tax variable in the short, medium and long term it is influenced by the tax itself and JUB. For the NPL variable in the short, medium and long term it is influenced by JUB and tax. For the CAR variable in the short, medium and long term it is influenced by JUB and tax. Then the results of the ARDL Panel analysis show that the country that is able to become a leading indicator in controlling the economic recession in the Four of The Group Twenty, namely Turkey, is only done by interest rates. While South Africa is done by interest rates, taxes, NPL, and CAR. For Russia, it is done by all variables, namely the amount of money in circulation, interest rates, exchange rates, taxes, NPL, and CAR. Meanwhile, Indonesia is carried out by exchange rates, taxes, NPL and CAR.

As a developing country, Indonesia has an economic structure dominated by the agricultural sector, making it vulnerable to disruptions to economic stability. Economic growth is an indicator showing that the economic level of society in general has increased in terms of consumption habits and people's purchasing power for goods and services. However, excessive consumption leads to a consumer society and inflation. This study aims to look at the causality between inflation, consumer price index, interest rates, gross domestic product, and exchange rates using the time series approach in the form of the quarterly period 2014Q1 to 2022Q2 in Indonesia using the VAR (Vector Autoregression) method. The research results show that inflation is related to or influenced by the consumer price index and savings. As for the Exchange Rate, Gross Domestic Product and Interest Rates have no effect on inflation.