Testing for Data Stationarity

Oct 13, 2025 by wbrum.org 35 views

The Effectiveness of the Use of Arima and Var in Projecting Credit Demand in Indonesia

Introduction

The Indonesian economy is one of the largest in Southeast Asia, with a growing middle class and increasing demand for credit. As a result, understanding the dynamics of credit demand is crucial for financial institutions, policymakers, and businesses. In this study, we examine the effectiveness of two popular time series forecasting methods, ARIMA (Autoregressive Integrated Moving Average) and VAR (Vector Autoregressive), in projecting credit demand in Indonesia.

Background

Credit demand is a critical component of the Indonesian economy, with significant implications for economic growth, employment, and poverty reduction. However, forecasting credit demand is a complex task, as it is influenced by various factors, including interest rates, inflation, GDP growth, and other macroeconomic variables. In recent years, there has been a growing interest in using time series forecasting methods to predict credit demand.

ARIMA Method

The ARIMA method is a widely used time series forecasting technique that involves several stages, including:

Testing for Data Stationarity: This involves checking whether the data is stationary or not. Stationary data is characterized by a constant mean and variance over time.
Integration: This involves determining the degree of integration of the data, which is measured by the number of times the data needs to be differenced to become stationary.
Unit Root Test: This involves testing whether the data has a unit root or not. A unit root is a characteristic of non-stationary data.
Correlogram Analysis: This involves analyzing the autocorrelation and partial autocorrelation functions of the data to identify patterns and relationships.
Model Estimation: This involves estimating the parameters of the ARIMA model using the data.

In this study, we used six different ARIMA models, including (1,1,0), (0,1,1), (1,1,1), (2,2,0), (0,2,2), and (2,2,2). The results showed that the ARIMA (1,1,0) and ARIMA (0,1,1) models were significant, with F-static values of 30,38796 and 58,92521, respectively. However, the ARIMA (1,1,0) model was considered more appropriate for projecting credit demand, as it showed a smaller average error (RMSE) of 8.70.

VAR Method

The VAR method is another popular time series forecasting technique that involves several stages, including:

Stationary Testing: This involves checking whether the data is stationary or not.
VAR Model Estimation: This involves estimating the parameters of the VAR model using the data.
Impulse Response Analysis: This involves analyzing the response of the variables to shocks or changes in the data.
Variance Decomposition: This involves decomposing the variance of the variables into their respective components.
Projection: This involves using the VAR model to project the values of the variables into the future.

In this study, we used the VAR method to project credit demand in Indonesia. The results showed that inflation and jibor data tended to increase, while the projection using the ARIMA method with the number of credit data showed a decline. This indicates a correlation between increased jibor, inflation, and credit demand.

Comparison of ARIMA and VAR Methods

The results of this study showed that the ARIMA method was more effective in projecting credit demand in Indonesia compared to the VAR method. This is because the ARIMA method has a smaller average error rate compared to VAR. Although the VAR method involves several stages to see the results of the projection, the level of accuracy is still not comparable to the ARIMA method.

Implications of the Study

This study has several important implications for stakeholders in the financial sector, including banks and other financial institutions. By using the ARIMA method, they can project credit demand accurately and plan a more effective business strategy. Additionally, the study highlights the importance of using time series forecasting methods to predict credit demand, which can help policymakers and businesses make informed decisions.

Conclusion

In conclusion, this study examines the effectiveness of two popular time series forecasting methods, ARIMA and VAR, in projecting credit demand in Indonesia. The results show that the ARIMA method is more effective in projecting credit demand compared to the VAR method. This study has important implications for stakeholders in the financial sector and highlights the importance of using time series forecasting methods to predict credit demand.

References

Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. Holden-Day.
Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer.
Tsay, R. S. (2002). Analysis of financial time series. Wiley.

Future Research Directions

This study highlights the importance of using time series forecasting methods to predict credit demand. Future research directions include:

Using other time series forecasting methods: This study only examined the effectiveness of ARIMA and VAR methods. Future research can explore the effectiveness of other time series forecasting methods, such as exponential smoothing, seasonal decomposition, and machine learning algorithms.
Using other data sources: This study only used credit data from the Bank of Indonesia. Future research can explore the effectiveness of using other data sources, such as macroeconomic data, financial data, and demographic data.
Examining the impact of economic shocks: This study only examined the effectiveness of ARIMA and VAR methods in projecting credit demand. Future research can explore the impact of economic shocks, such as recessions and financial crises, on credit demand.
Frequently Asked Questions (FAQs) about the Effectiveness of Arima and Var in Projecting Credit Demand in Indonesia

Q: What is the main objective of this study?

A: The main objective of this study is to examine the effectiveness of two popular time series forecasting methods, ARIMA and VAR, in projecting credit demand in Indonesia.

Q: What are the key findings of this study?

A: The key findings of this study are that the ARIMA method is more effective in projecting credit demand in Indonesia compared to the VAR method. The ARIMA method has a smaller average error rate compared to VAR, and it is more accurate in predicting credit demand.

Q: What are the implications of this study for stakeholders in the financial sector?

A: The implications of this study for stakeholders in the financial sector are that they can use the ARIMA method to project credit demand accurately and plan a more effective business strategy. This can help them make informed decisions and reduce the risk of financial losses.

Q: What are the limitations of this study?

A: The limitations of this study are that it only examined the effectiveness of ARIMA and VAR methods in projecting credit demand, and it only used credit data from the Bank of Indonesia. Future research can explore the effectiveness of other time series forecasting methods and use other data sources.

Q: What are the future research directions based on this study?

A: The future research directions based on this study are to use other time series forecasting methods, such as exponential smoothing, seasonal decomposition, and machine learning algorithms, to project credit demand. Additionally, future research can explore the impact of economic shocks, such as recessions and financial crises, on credit demand.

Q: What are the benefits of using the ARIMA method in projecting credit demand?

A: The benefits of using the ARIMA method in projecting credit demand are that it is more accurate and has a smaller average error rate compared to VAR. It also allows for the identification of patterns and relationships in the data, which can help in making informed decisions.

Q: What are the benefits of using the VAR method in projecting credit demand?

A: The benefits of using the VAR method in projecting credit demand are that it allows for the analysis of the relationships between multiple variables and the identification of the impact of shocks on the system. However, it has a larger average error rate compared to ARIMA.

Q: What are the challenges of using time series forecasting methods in projecting credit demand?

A: The challenges of using time series forecasting methods in projecting credit demand are that they require a large amount of data and can be sensitive to the choice of parameters and model specifications. Additionally, they may not capture all the complexities of the system, such as non-linear relationships and structural breaks.

Q: What are the future applications of this study?

A: The future applications of this study are in the fields of finance, economics, and business. It can be used to project credit demand in other countries, to analyze the impact of economic shocks on credit demand, and to develop more accurate forecasting models.

Q: What are the contributions of this study to the existing literature?

A: The contributions of this study to the existing literature are that it provides a comprehensive analysis of the effectiveness of ARIMA and VAR methods in projecting credit demand in Indonesia. It also highlights the importance of using time series forecasting methods in projecting credit demand and provides a framework for future research.

Q: What are the recommendations for future research based on this study?

A: The recommendations for future research based on this study are to use other time series forecasting methods, to explore the impact of economic shocks on credit demand, and to develop more accurate forecasting models. Additionally, future research can examine the effectiveness of other data sources, such as macroeconomic data, financial data, and demographic data.