Monetary policy refers to the policies implemented by the bank's monetary authority to maintain and stabilize the economy. The execution of monetary policy involves the use of several monetary instruments that operate through a monetary policy transmission system. Monetary policy transmission is a conceptual framework that explains the impact of policy implementation on economic activity. (Bernanke & Gertler, 1995) stated that monetary policy transmission can be controlled through the credit channel mechanism, which consists of the balance sheet channel (BSC) and the bank loan channel (BLC). Current studies state that in developing countries, the bank lending channel (BLC) is weak (Abuka et al., 2019), and as in Indonesia, studies on BLC are limited in a market context (Soedarmono et al., 2021). (Besley et al., 2012) stated that credit lines, as bank landing channels, have an important meaning in an economy where the banking sector plays a dominant role. Therefore, banks have a role in their existence and governance so that they can understand how monetary policy should be implemented in the economy.

In the last 12 years, the percentage of economic growth in Indonesia can be said to have had a downward trend, but the value did not drop drastically, 6.81% (2010) to 5.01% (2022), and in that year there were several policies that were less effective. CEIC data (global economic data, indicators, charts, and forecasts) for Indonesia showed that the amount of bank credit distributed fluctuates, leading to unstable conditions. The highest credit growth occurred in 2011 at 24.64%, in line with an increase in GDP of 5.94% and an increase in inflation of 4.11%. The rapid increase in demand for credit was the result of increasing public needs. Furthermore, a drastic decline in credit occurred in 2020, -2.63% as a result of a decrease in real GDP of -2.17% and inflation, 1.57%. The emergence of the COVID-19 Pandemic exacerbated this condition. The COVID-19 pandemic has had an impact on the banking sector, a significant decline in credit growth (IMF, 2020). The decline in lending during the crisis and the slow growth in lending are often cited as one of the factors driving Indonesia's economic recovery. Base on CEIC data, also illustrates that developments between credit, credit interest rates, and inflation rates have different movements. During the COVID-19 pandemic (2019-2021), there was a decrease in credit rates, which is very drastic, even though we can see that interest rates have been reduced (fig.1). The central bank has taken various expansionary measures during the pandemic, such as gradually lowering the benchmark interest rate since the beginning of the COVID-19 pandemic until now, reached 3.5% to boost demand for credit (Bank Indonesia, 2021), In fact, monetary policy, by reducing interest rates, or what is called expansionary policy, it turns out that credit growth still tends to experience a decline.

The monetary policy of a country also affects its economy through the bank lending channel, which directly affects the availability of bank loans. (Fungáčová et al., 2021) found in their study that the supply of bank lending is influenced by monetary policy due to the fact that bank lending and government bonds are not completely interchangeable as assets for banks. The effectiveness of the bank lending channel in implementing monetary policy relies heavily on the availability of loans and several factors that influence both consumer behavior and the corporate environment. The impact of a restrictive monetary policy is observed in the reduction of bank reserves and deposits. Banks will decrease lending, leading to a detrimental effect on output. Conversely, implementing an expansionary monetary policy will boost bank reserves and output (Apergis et al., 2012). (Modugu & Dempere, 2022) proved that transmission through the bank lending channel is contingent on whether the established monetary policy is contractionary or expansionary (e.g., interest rates or money supply), despite the fact that the latter remains ineffective at inducing credit contraction. (Modugu & Dempere, 2022) have asserted that the influence of monetary policy on the effectiveness of transmitting monetary policy, particularly in developing countries, is weak when it comes to the bank lending channel and the manipulation of interest rates. Due to impediments in financial institutions, markets, and bank concentration, this condition develops. (Borio & Gambacorta, 2017) included liquidity, capitalization, funding costs, bank risks, and income, and found that the effectiveness of short-term interest rate reductions in promoting the growth of financing from banks decreases when interest rates hit exceptionally low levels. This finding is valid even after considering aspects such as business and financial cycle conditions, as well as different bank-specific diversification.

A great deal of investigation to examine the influence and interplay between credit, monetary policy, and economic growth in relevant studies. It is imperative to consistently monitor the presence of credit to ascertain appropriate monetary policies ((Besley et al., 2012); (Gambacorta, 2003); (Manaresi & Pierri, 2019)).(Khan, 2015) observed in Pakistan, 1% rise in inflation leads to a decrease in lending by approximately 0.16%. (Almalki & Batayneh, 2015) provide evidence of a negative correlation between inflation and bank lending in Saudi Arabia, with a coefficient of -2.65. (Zermeño et al., 2018) conducted an analysis of inflation and credit across 84 countries from 1980 to 2010. Employing a quantitative panel regression approach, it was found that the association between these two variables exhibited a non-linear pattern. Specifically, the relationship was found to be statistically significant for developing countries. (Tinoco-Zermeño et al., 2022) stated the inflation rate exerts a detrimental influence on bank lends in Mexico. However, in the short term, the impact tends to be beneficial. In addition to the instruments, (Iddrisu & Alagidede, 2020) discovered that the implementation of interest rates and lending channels as a proportion of monetary policy limitations resulted in a reduction of banking sector credit by 0.22%. (Modugu & Dempere, 2022) conducting research in developing countries in Sub-Saharan stated the money supply has a significant impact on credit channels, whereas the monetary policy rate has a minimal effect on bank lending activities. The limited influence of the monetary policy rate on lending can be attributed to the inadequate degree of financial development, high bank concentration, institutional vulnerability, and various other structural characteristics that tend to diminish the effectiveness of monetary policy transmission in developing nations. Bank's capital adequacy ratio and macroeconomic conditions, such as GDP growth rate, are key factors that impact bank lending.

Theoretically, the standard concept of the monetary policy transmission mechanism starts with monetary policy influencing the development of various interest rates in the financial sector, as a determination of interest rates, which become a reference for determining other interest rates, especially credit interest rates. (Borio & Gambacorta, 2017) state interest rates on credit have an impact on the demand for credit and an effect on inflation. Empirically, it is a bit contradictory to existing theory because even though credit interest rates have increased or decreased, demand for credit by the public continues to increase, so that it often puts pressure on rising prices (inflation) in certain sectors. Based on the background and facts presented, there are problems related to credit levels, inflation, and GDP related to the monetary policy mechanism. The bank landing channel problem is complex and must be solved immediately.

We use the vector autoregression (VAR) analysis technique to observe the shock of monetary policy. (Abraham et al., 2023) found that VAR model can be used to measure the impact of interest rate changes on an interest rate variable over a long period of time, for example, up to twelve months using monthly data. This analysis technique is not the first to be used. Various studies related to monetary policy, in particular bank channel lending, have used this model. (Aysan et al., 2018) employed a VAR panel model with quarterly data from Turkey and found that the responses of sharia banks to policy interest rate changes, in terms of savings and credit, were greater. They associated these differences in responses with the sharia bank's reaction moisture, while conventional banks were more prepared to accommodate policy rate changes. During the adjustment period, sharia bank deposits can withdraw their savings if the returns offered by alternative investments become more profitable. Study by (Karlsson et al., 2023) proved that the vector autoregression (VAR) model has emerged as a crucial macroeconomic model utilized by policymakers and forecasters. In order to analyze a correlation between the GDPPC and the selected independent variables (Corruption Perception Index, Political Rights score, Civil Liberties score, Gender Inequality Index, Consumer Price Index, Population Density, and the percentage of people using the Internet), (Ganeriwalla & Mehta, 2021) also combined OLS and VAR. (Matĕjů, 2019) combined OLS with VAR to improve the identification of structural shocks through the application of restrictions (e.g., zero or sign restrictions). This is crucial for understanding the monetary transmission mechanism and how policy changes propagate through the economy.

This article highlights the importance of maintaining credit stability for Indonesia's economic growth, especially considering the trend of declining growth despite the volatile distribution of credit. The breakdown of the urgency of this research is as follows: 1) The historical correlation from Indonesian credit growth (Figure 1) showed that 2010–2022, economic growth showed a downward trend (6.81% to 5.01%), coinciding with fluctuations in bank credit distribution. In 2011, the highest credit growth (24.64%) aligned with a rise in GDP (5.94%) and inflation (4.11%). This suggests credit availability fuels economic activity, and then in 2020, a drastic credit decline (-2.63%) mirrored a drop in GDP (-2.17%) and inflation (1.57%). The COVID-19 pandemic exacerbated this, highlighting the impact of unstable credit on economic resilience. This condition's impact emphasizes the link between slow credit growth and Indonesia's sluggish economic recovery post-pandemic. Even though Bank Indonesia has made various efforts to stimulate credit demand through interest rate cuts (expansionary policy), credit growth remains stagnant despite the lowered interest rates. This scenario underscores the need for stable credit to ensure economic growth. Fluctuations create uncertainty and hinder business activity and investment. Therefore, maintaining stable credit is crucial for Indonesia's economic well-being. It fuels growth, strengthens resilience during crises, and facilitates a more predictable economic environment.

The purpose of this study is to carry out a descriptive analysis of the development of national and provincial credit in Indonesia, to estimate what factors influence loans in Indonesia in order to increase economic growth, to make estimates, and to analyze the results of the estimation on the effectiveness of landing channel banks in transmitting policies. monetary system in Indonesia.

This study used a quantitative approach to estimate and analyze the relationship between the variables that have been determined to answer the problem. We used secondary data in this study, monthly data starting from the 2010–2022. The data used in this research comes from Bank Indonesia, the Financial Services Authority, Indonesian Statistics, and CEIC Data (Global Economic Data, Indicators, Charts, and Forecasts).

In this groundbreaking study, we will elucidate the intricate relationship between credit, interest rates, inflation, and economic growth using the powerful Ordinary Least Squares (OLS) analysis technique. Moreover, we will delve into the dynamic interplay of these variables, demonstrating how past values influence their current and future trajectories, employing the cutting-edge vector autoregression (VAR) analysis technique. This research promises to unlock invaluable insights into the forces driving economic dynamics, offering actionable intelligence for informed decision-making.

Both of these analysis techniques have their advantages. Ordinary Least Squares (OLS) is a method used in linear regression to find the relationship between the dependent variable and one or more independent variables by minimizing the sum of the squares of the residuals (the difference between the predicted values and the actual values). OLS is superior in estimating the causal effects of one variable on another. In this case, we can isolate the direct impact of credit availability on economic growth (Y) while controlling for the effects of interest rates (X1) and inflation (X2). The method is similar to the approach used in (Ganeriwalla & Mehta, 2021). Using OLS will help identify the direct causal effects of credit on growth, which is valuable for policymakers aiming to stimulate growth through credit expansion.

Meanwhile, VAR considers how the past values of all variables (credit, interest rates, inflation, and growth) can affect the current and future values of the variables. (Lütkepohl, 2005) mentions that VAR provides a comprehensive picture of how these variables interact. The VAR model also shows the possibility of simultaneous effects and can be used to predict the future values of these variables to help policymakers anticipate potential economic trends.

The empirical model in this study is as follows:

Creditt = β0 + β1BIRatet + β2GDPt + β3IRt + β4INFt + εt

Where Lendt: Total Credit; BIRatet: interest rate in year t, GDPt_: GDP in year t, IRt: credit interest rate in year t; INF: inflation rate in year t, β0: constant, β1-β4: regression coefficients, t: time unit (2010-2022), εt: residuals.

We used VAR to find out how effective the bank's landing channel system is in transmitting monetary policy developments in Indonesia. Abraham et al. (2023) stated that this approach is widely preferred due to its capability of accepting endogenous inputs for all variables in the model. The model used to analyze the data as follow:

Xt = β0 + βnXt − n + et

Where Xt is a vector element of Creditt, BIRatet, IRt, INFt, GDPt, β0 is a constant vector n x 1, βn is a coefficient of Xt, and n is the lag length.

Java's substantial population, coupled with its role as an economic center, has led to a higher level of credit use compared to other islands. Additionally, its well-developed financial system and infrastructure facilitate easy access to credit.

Bank Indonesia implements monetary policy in Indonesia with the goal of achieving stability in the value of the Rupiah, maintaining stability in the payment system, and maintaining stability in the financial system to support sustainable economic growth. Therefore, we reviewed the monetary policy transmission mechanism through the bank lending channel by observing the COVID-19 pandemic. We observed how the bank lending channel system affected total credit in Indonesia during 2010–2022. We use the OLS technique and complement it by using VAR to see its effectiveness two years after the policy was implemented.

However, in order to conduct further analysis on credit within monetary policy, it would be beneficial to carry out a thorough credit analysis, especially by taking into account the fluctuations in credit variance. This is a crucial step in measuring and managing credit risk in the future. Further research in this field is essential to refine existing models and develop new methods that are more adaptive to monetary policy in Indonesia. By understanding the patterns of credit variance changes, financial institutions can formulate more accurate monetary policies through credit. In addition, the results of this research can also provide significant contributions to the development of better banking regulations, thereby protecting the interests of customers and maintaining the stability of the financial system. In addition, the VAR approach made it easier to capture all time variations for the coefficients, as well as the variance in shock the recommendations to policy maker we summarize the results below

First, policymakers could consider implementing more targeted monetary policy measures, such as stimulating credit growth in specific sectors or regions. Second, investing in infrastructure development, particularly in regions with high concentrations is crucial. For further studies, we suggest to observe regional disparities in credit access and their implications for economic development. By implementing these recommendations, policymakers and stakeholders can contribute to a more inclusive and sustainable financial system in Indonesia, fostering economic growth and development

I would like to express my gratitude to my friends, including FEB UMSU, for their invaluable support in completing this article on monetary economics. Their assistance has been instrumental in my journey toward becoming a Senior Lecturer.