Etihad: Journal of Islamic Banking and Finance P-ISSN 2807-730X.
E-ISSN 2807-6915 Volume 5 Issue 2.
July - Desember 2025 https://jurnal.
id/index.
php/etihad Identifying Measurable Impact of Islamic Banking on the Indonesian Economy Haryani Santo Hartono1*.
Rininta Nurrachmi2 1,2 Universitas Islam Internasional Indonesia.
Indonesia E-mail: haryani.
hartono@ui.
id, rnurrachmi@gmail.
Article Info Article history:
Received September 30, 2025 Revised November 19, 2025 Accepted December 02, 2025 *Corresponding author email :
hartono@ui.
Keywords:
Indonesia.
Islamic banking.
Islamic finance.
Economic DOI: 10.
21154/etihad.
Page: 130 Ae 144 Abstract Introduction: This study investigates the shortand long-term relationship between Islamic banking development and IndonesiaAos economic Despite Indonesia operating a dual banking system, empirical research on Islamic Understanding this connection is crucial for leveraging Islamic finance as a driver of sustainable development.
Research Methods:
Using quarterly data from 2015 to 2024, the study examines the dynamic links among Islamic financing.
GDP, and gross fixed capital formation (GFCF).
The ARDL bounds testing approach, cointegration analysis, and error correction model (ECM) are employed to identify both short- and long-run relationships.
Model stability is validated using CUSUM and CUSUMSQ tests.
Results: The results reveal a strong long-term relationship between Islamic financial development, capital accumulation, and economic growth, supporting the supply-leading hypothesis.
Short-term effects are positive but statistically insignificant.
Model stability tests confirm structural consistency over Conclusion: These findings emphasize the potential of Islamic finance to promote sustainable economic development and provide guidance for policymakers seeking to enhance financial inclusion and resilience.
Etihad with CC BY license.
Copyright A 2025, the author.
Etihad: Journal of Islamic Banking and Finance | 130 Haryani Santo Hartono.
Rininta Nurrachmi INTRODUCTION The connection between economic growth and the financial sector is one of the main problems of development economics.
A variety of various theories have been used to explain the dynamics of this relationship, some of them being the supply-leading hypothesis, which argues that the financial sector is the major driver of economic growth (Patrick, 1.
and the demandfollowing hypothesis, which argues that the real sector is the major driver of financial system growth (Hermes & Lensink, 1.
In fact, a number of empirical studies indicate that the two directions are causal (Levine.
Loayza, & Beck, 2.
Empirical research has placed much emphasis on this relationship in the last several decades.
As suggested by Levine (Levine, 2.
, a well-developed financial system is necessary in terms of encouraging economic growth as it becomes easy to mobilize savings, allocate resources, oversee businesses, handle risk, and exchange goods and services.
Most of the early research, starting with Goldsmith .
provided the empirical basis of the studying of the causal relationship between finance and economic Patrick .
also came up with two main assumptions namely the supply-leading and the demand-following assumptions.
Supply-leading implies that the growth of the financial sector drives economic growth and demand-following implies that economic growth drives demand of more developed financial services.
Empirical research like Rioja and Valev .
which compared 74 countries of varied income levels has determined the heterogeneity in effects, financial development was significant in growth of middle-income countries, but less evident in low-income countries.
Also.
Levine.
Loayza and Beck .
showed through dynamic panel GMM estimation that the indicators of financial development including the measures of liquidity in the financial systems and ratio of the private sector credits in the countries are significant contributors to the difference in the growth across the nations.
These results support the fact that financial development is an important driver of economic growth.
Meanwhile, some of the researchers suggest that finance-growth relationship is not as straightforward as a one-directional causality relationship.
Arestis.
Demetriades, and Luintel .
emphasize that it is not enough to consider the banking or capital market, but a dualistic approach is required due to the fact that the countries differ in the way these institutions contribute to the growth.
Recent studies indicate that the association relies on the conditions of structures, development levels, and financial systems composition (Cull.
Demirgyy-Kunt, & Morduch, 2.
Although this is growing literature in the world, majority of the studies are based on traditional financial systems.
Quantitative, empirical or econometric studies undertaken under the Islamic finance framework are still few, and the ones that do exist tend to be studies involving the relationship between Islamic banking development and macroeconomic growth.
This is a major disconnection since Islamic finance is conducted under unique values, profit-and-loss Annual International Conference on Islamic Economics (AICIE) | 131 Haryani Santo Hartono.
Rininta Nurrachmi sharing, the usury, and real asset-backed dealings, which means that its avenues of influence might not be the same as traditional finance.
There is still a paucity of empirical studies in Islamic finance.
An example is where Abduh and Chowdhury .
2 ) affirmed the supply-leading hypothesis in Bangladesh and Indonesia and they found that there were long term relations between Islamic banking growth and economic Meanwhile.
Yusof and Bahlous .
found the bidirectional causality in the countries of the Middle East and North Africa which can suggest that the system of Islamic finance can act differently in response to institutional formations.
In a microeconomic scale.
Hasan.
Wachtel, and Zhou .
indicated that Islamic banking may positively affect the growth of SMEs in the financially marginalized societies implying its possible effects on inclusive development.
The theoretical and empirical context is particularly applicable in Indonesia, which is home to the largest number of Muslims in the world, and the most rapidly expanding Islamic banking sectors.
There is an increased growth of the Islamic banking industry in Indonesia.
According to the Financial Services Authority .
, the financial services industry is experiencing an annual growth of 13.
8% in assets of national Islamic banking with IDR 265 trillion in 2014 that experienced an increase to IDR 837 trillion in 2023.
Nevertheless, the market share of Islamic banking in Indonesia is relatively small at 7.
5% despite the robust asset growth (OJK.
This provides both a challenge and an opportunity: although the industry has a good momentum, it is highly fragmented with the greater economy, and whether it has a real macroeconomic influence should be questioned.
This is necessary to assess the role of Islamic banks in the overall economic development of Indonesia and how the growth of these banks will impact the macroeconomic performance both in the short-term and long-term (Ascarya & Yumanita, 2.
(Rusydiana, 2.
However, there are few studies that have studied the co integration between Islamic financial development and national economic growth in Indonesia especially in the short and long term.
Still less have used methodologies that can use the limited time-series data and dynamic adaptations.
The Autoregressive Distributed Lag (ARDL) Bounds Testing Approach is just one such method which especially is appropriate in small samples, and gives a solid understanding of long-term relations, yet is not extensively used in the studies of Islamic finance.
It is worth noting that to understand how the situation of Islamic financial development in Indonesia evolved it is necessary to refer to the evolution of the country in the past.
Islamic banking in Indonesia started with the foundation of Bank Muamalat Indonesia in 1991, and has expanded a lot following the financial crisis of 1997-1998 by the promotion of diversification of financial system that favored the growth of Islamic banking (Ascarya & Yumanita, 2.
This was a significant milestone when PT Bank Susila Bakti was converted to Bank Syariah Mandiri in 1999.
Since that, the growth has improved with the expansion of the Islamic commercial banks (BUS), the Islamic business units (UUS) and the Islamic rural banks (BPRS).
Indonesia has 12 BUS, 20 UUS, and more than 160 BPRS today, which is both indicative of institutional growth and demand.
Annual International Conference on Islamic Economics (AICIE) | 132 Haryani Santo Hartono.
Rininta Nurrachmi Governmental policies, including KNEKS and Masterplan of the Indonesian Islamic Financial Architecture (MAKSI), have been important in the systematic and quantifiable growth of the industry (Komite Nasional Ekonomi dan Keuangan Syariah (KNEKS), 2.
Legitimacy and clarity of regulation are also highly supported by the legal base particularly the law number 21 of 2008 on Sharia banking.
Growth is also driven by demand-side factors, especially increased financial literacy of MSMEs and younger demographics, due to increasing Islamic financial literacy (Bank Indonesia, 2.
The financial performance indicators also reveal that the Islamic banks have recorded a good health.
Normal financing-to-deposit ratios (FDR) are typically in the range of 80 and 100 (Bank Indonesia, 2.
, whereas non-performing financing (NPF) is usually around 3-4 .
s compared to conventional banking non-performing loans of 5-.
(OJK, 2.
Islamic banking is also found to be positively correlated with GDP growth associated with financing the real sector, and empowering the MSMEs (Hosen and Muhari, 2.
Islamic finance is now likely to become a strategic instrument to improve resiliency, inclusion, and sustainable development due to the projected stable economic growth of 5-6% annually in Indonesia (World Bank, 2.
Nonetheless, empirical studies on the relevance of Islamic financial development in fact linking with macroeconomic growth in Indonesia are few and inconclusive in spite of the rapid growth and economic potential of the industry.
This study seeks to address existing research gapsAinamely the limited examination of short- and long-run cointegration between Islamic finance and national economic growth, the insufficient application of ARDL and ECM models in the Indonesian context, and the need for country-specific analyses of dual financial systemsAiby investigating the correlation between IndonesiaAos economic growth and the evolution of Islamic finance in both the short and long term, as a basis for clarifying the actual role of the Islamic financial system in supporting the national economy.
The research has these objectives and contributes to the work of Abduh & Azmi Omar .
by making several significant improvements.
Firstly, it applies the ARDL and the error correction model (ECM) which are quite rare in the literature of Islamic finance, particularly in developing nations.
Second, it narrows down to Indonesia with a particular dual banking structure and a rapidly growing market in the Islamic finance market of the world.
Third, it has a theoretical and practical contribution since it evaluates the possibility of Islamic finance as an effective channel of macroeconomic growth in those countries where Islamic and conventional financial systems are run in parallel.
RESEARCH METHOD
Data Together with two variables that represent the real economic sector, namely GDP .
] and gross fixed capital formation .
], this study used quarterly time series data for Annual International Conference on Islamic Economics (AICIE) | 133 Haryani Santo Hartono.
Rininta Nurrachmi the variable total financing of Islamic banks .
] as a representation of the Islamic finance The IMF's International Financial Statistics and Bank Indonesia's monthly statistical report on Islamic banking provided the time series data, which spans the first quarter of 2015 to the fourth quarter of 2024.
The following is descriptive data resulted from this study.
Table 1.
Descriptive Statistics Source: Processed Data, 2025 Mean Median Maximum Minimum Std.
Dev.
Skewness Kurtosis LNFIN
LNGDP
LNGFCF
Jarque-Bera Probability Sum Sum Sq.
Dev.
Observations One often used statistic to show a nation's income level over a given LNFIN time period is the gross domestic product, or GDP.
GDP is typically regarded 0 as the primary variable that represents economic growth in studies on the connection between the financial sector and economic Because it is an economic indicator that12.
6measures net new investments made by 4 a specific accounting period, gross fixed businesses in the form of domestic fixed assets during capital formation, or GFCF, is used as a representation of investment.
Below are graphs from each variable.
LNFIN
LNGDP
Figure 2.
GraphLNGDP of Variable Lnfin Source: Processed Data, 2025 Figure 1.
Graph LNGFCF of Variable Lngdp Source: Processed Data, 2025 Annual International21.
Conference on Islamic Economics (AICIE) | 134 Haryani Santo Hartono.
Rininta Nurrachmi LNGFCF Figure 3.
Graph of Variable Lngfcf Source: Processed Data, 2025 Based on quarterly data from 2015Q1 to 2024Q4 .
total of 37 observation.
, the VAR model involving the variables LNFIN.
LNGDP, and LNGFCF has an ideal lag length of lag 1, according to the output of the VAR Lag Order Selection Criteria.
The majority of the selection criteria, such as the LR (Likelihood Rati.
FPE (Final Prediction Erro.
AIC (Akaike Information Criterio.
SC (Schwarz Criterio.
, and HQ (Hannan-Quinn Criterio.
, support this by designating lag 1 with an asterisk (*), signifying that it is the recommended option.
According to this, it is advised to use a single lag for VAR estimation since it offers the best trade-off between model fit and complexity.
Table 2.
Lag Length Criteria
Source: Processed Data, 2025
VAR Lag Order Selection Criteria
Endogenous variables: LNFIN LNGDP LNGFCF
Exogenous variables: C
Date: 06/22/25 Time: 05:18 Sample: 2015Q1 2024Q4
Included observations: 37
Lag
LogL
FPE
75e-12* AIC 56148* -17.
03902* -17.
* indicates lag order selected by the criterion LR: sequential modified LR test statistic .
ach test at 5% leve.
FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion Annual International Conference on Islamic Economics (AICIE) | 135 Haryani Santo Hartono.
Rininta Nurrachmi Following the collection of all required data, the Augmented Dickey-Fuller (ADF) and Phillips-Perron (P-P) methods are used to perform a unit root test on each variable .
efer to Table The unit root test is still carried out to make sure the ARDL model is suitable for use, even though the ARDL approach does not call for testing for the stationarity prerequisite.
Testing using the ARDL approach can proceed because the results in Table 2 show that a combination of variables are stationary at level I.
and at first difference I.
Table 3.
Unit Root Test Source: Processed Data, 2025 Augmented Dickey-Fuller Phillips-Peron Note: Significant at : * 5 and ** 1 percent levels Variable Lnfin Lngdp Lngfcf ARDL and ECM Regression elements on past values of the dependent variable .
and present and past values of the independent variables .
istributed la.
are combined in the ARDL (Autoregressive Distributed La.
model, an econometric technique.
Without first confirming that all variables are stationary at the same level, this approach makes it possible to analyze long-term relationships between variables.
The primary benefit of the ARDL approach is that it can be applied to time series data that includes both I.
and I.
This helps to prevent errors caused by the shortcomings of unit root tests, which are frequently insufficiently robust in small samples (Pesaran & Shin, 1.
As a result.
ARDL is now a versatile approach that is frequently employed in financial and economic research, particularly in situations where there are uncertainties about the degree of variable integration.
The ARDL approach to cointegration is implemented by estimating the conditional error correction (EC) model of ARDL, which does not require prior knowledge about the direction of long-term relationships among variables.
Three primary model forms are used in this study.
yuU yuU OIycoycuyciyccycyyc = yu0 Ocycn=1 ycn OIycoycuyciyccycyycOeycn Ocycn=0 OIycn OIycoycuyceycnycuycOeycn yu1 ycoycuyciyccycyycOeycn yu2 ycoycuyceycnycuycOeycn ycyc .
yuU yc OIycoycuyceycnycuyc = yu0 Ocycn=1 yuiOIycoycuyceycnycuycOeycn Ocycn=0 OIycn OIycoycuyciyccycyycOeycn yu1 ycoycuyceycnycuycOeycn yu2 ycoycuyciyccycyycOeycn ycyc .
yuU yuU OIycoycuyciyceycayceyc = yu0 Ocycn=1 oycn OIycoycuyciyceycayceycOeycn Ocycn=0 ycn OIycoycuyceycnycuycOeycn yu1 ycoycuyciyceycayceycOeycn yu2 ycoycuyceycnycuycOeycn ycyc .
Where:
Lngdp : natural logarithm of real gdp Lnfin : natural logarithm of Islamic banksAo total financing Lngfcf : natural logarithm of gross fixed capital formation Annual International Conference on Islamic Economics (AICIE) | 136 Haryani Santo Hartono.
Rininta Nurrachmi One of the main methods for determining whether there is a long-term relationship between variables is the F statistical test.
This test also aids in identifying which variable in the model should be normalized if cointegration is detected.
To determine whether the lagged levels of the variables in the unrestricted conditional error correction model are significant, the F test is used within the framework of the extended Dickey-Fuller regression.
Following confirmation of the long-term relationship, the pertinent equations are estimated using an ARDL model of order .
, .
(Harris & Sollis, 2003.
Odhiambo, 2.
yuU yc ycoycuyciyccycyyc = yu0 Ocycn=1 yu1 ycoycuyciyccycyycOeycn Ocycn=0 yu2 ycoycuyceycnycuycOeycn yuiyc .
yuU yc ycoycuyceycnycuyc = yu0 Ocycn=1 yu1 ycoycuyceycnycuycOeycn Ocycn=0 yu2 ycoycuyciyccycyycOeycn yuiyc .
yuU yc ycoycuyciyceycayceyc = yu0 Ocycn=1 yu1 ycoycuyciyceycayceycOeycn Ocycn=0 yu2 ycoycuyceycnycuycOeycn yuiyc .
Prior to estimating the chosen model using the Ordinary Least Squares (OLS) method, the number of lags in the ARDL model is determined using the Akaike Information Criterion (AIC) and the Bayesian Schwarz Criterion (SBC).
Following the initial estimation, the lag selection is verified again by analyzing the residual correlogram from the unrestricted VAR using the chosen lags and evaluating the lag length criteria.
The ideal lag is the one that yields the lowest value on the AICSBC and is backed by the findings of the lag length criteria and the insignificance of the residual correlogram probabilities in the unrestricted VAR.
The ARDL approach yields effective and objective estimates, particularly in studies with small sample sizes, claim Blackburne and Frank .
Its simultaneous estimation of long-term and short-term components helps prevent autocorrelation problems and bias from omitted variables, which is another benefit.
Furthermore, this approach makes it possible to distinguish clearly between the model's independent and dependent variables.
Short-term elasticity can also be obtained by building an Error Correction Model (ECM) in the manner described below, provided that cointegration is discovered in the ARDL equations .
yuU yuU OIycoycuyciyccycyyc = yu0 Ocycn=1 ycn OIycoycuyciyccycyycOeycn Ocycn=0 2 OIycoycuyceycnycuycOeycn yu yayaycNycOe1 yuyc .
yuU yc OIycoycuyceycnycuyc = yu0 Ocycn=1 ycn OIycoycuyceycnycuycOeycn Ocycn=0 2 OIycoycuyciyccycyycOe1 yu yayaycNycOe1 yuyc .
yuU yuU OIycoycuyciyceycayceyc = yu0 Ocycn=1 ycn OIycoycuyciyceycayceycOeycn Ocycn=0 2 OIycoycuyceycnycuycOeycn yu yayaycNycOe1 yuyc .
Where:
: The first difference of the operator Aos : Coefficients associated with the model's equilibrium's short-term dynamics yu : Measures the speed of adjustment Annual International Conference on Islamic Economics (AICIE) | 137 Haryani Santo Hartono.
Rininta Nurrachmi An adaptive parameter that gauges transient departures from long-term equilibrium conditions is the error correction component with a lagged parameter, also known as the Error Correction Term or ECT.
Variables may diverge in the short term, which will eventually impact the system's ability to reach equilibrium.
Consequently, the existence of an error correction mechanism that returns these variables to their long-term relationship is demonstrated by the statistical significance of the ECT coefficient.
RESULT AND DISCUSSION
A Granger causality test was performed on the suggested variables prior to determining whether there was a long-term relationship between Islamic bank financing and these variables.
the findings are shown in Table 3.
The test's findings suggested a supply-side .
upply-leadin.
one-way causal relationship between Islamic bank financing and GDP growth.
An examination of the reciprocal relationship between Islamic financing and growth is still required, though, as shown by equations .
Estimating the long-term relationship as it is expressed in equations .
is the next stage.
The lowest values of the AIC and SBC criteria, lag length selection methods, and observations of the residual correlogram's insignificance from the unrestricted VAR are all used to determine the ideal number of lags.
Based on these findings, one lag should be used for all Table 4.
Granger Causality Test Source: Processed Data, 2025 Null Hypothesis LNGDP does not Granger Cause LNFIN LNFIN does not Granger Cause LNGDP LNGFCF does not Granger Cause LNFIN LNFIN does not Granger Cause GFCF F-Statistic Probability 0781 ** Note: Significant at: *5 percent level Estimating the long-term relationship in equations .
Ae.
is the next stage.
The ideal number of lags to include in the model is determined by combining the smallest value of AICAe SBC, the lag-length criteria technique, and observation towards the insignificant correlogram of residuals from unrestricted VAR.
For Equation .
, the selected lag length is 1, and for Equations .
, it is 2.
Table 4 shows the Wald-test computed F-statistics and the bound critical values proposed by Narayan .
For Equations .
, the corresponding computed Fstatistics are 9.
1638, 4.
8458, and 4.
Equation .
, with the restricted intercept and no trend specification, surpasses the upper bound critical value at the 1% significance level .
Under the same specification, equations .
also surpass the upper bound at the 5% significance level .
Thus, there is Annual International Conference on Islamic Economics (AICIE) | 138 Haryani Santo Hartono.
Rininta Nurrachmi evidence of a long-term relationship between Indonesia's economic performance and Islamic financial development, rejecting the null hypothesis that there is no cointegration between the financing of Islamic banks and the variables examined at different levels of significance.
Table 5.
F-Statistic of Cointegration Relationship and Bound Critical Values Source: Processed Data, 2025 Equation Lag F-statistic .
Sig.
level (%) Bound Critical Values Restricted Intercept Restricted and No Trend Intercept and Trend As shown in Table 5, in equations .
, the variable of Islamic financing seems to be significant, and its sign is constant throughout the models.
This consistency suggests that the coefficient should be positiveAias both equations confirmAiif Islamic financial development makes a substantial contribution to capital formation and economic growth.
A positive correlation implies that the real sector's performance, especially GDP and gross fixed capital formation (GFCF), improves with the level of development of the Islamic financial system.
As a result, the leading sectors in the growth process can be supported and maintained by the financial system.
However, as shown by the negligible GDP coefficient in equation .
, we do not find any compelling evidence that economic growth (GDP) eventually supports the development of Islamic finance.
This suggests that although Islamic financial development seems to have a positive impact on capital formation and economic growth, the opposite relationship is not statistically significant.
Accordingly, the long-run model suggests that the relationship between Indonesia's Islamic financial development and growth is unidirectional, with Islamic finance driving growth rather than bidirectional.
Annual International Conference on Islamic Economics (AICIE) | 139 Haryani Santo Hartono.
Rininta Nurrachmi Table 6.
Long-run Model Source: Processed Data, 2025 Coefficient of Lnfin Coefficient of Lngdp Dependent Variable Lngfcf 0887 .
Lngdp 0803 .
Lnfin 1719 .
Note: Significant at 5 percent level ECM-based causality analysis was performed for all three equations based on the cointegration test results.
the results are shown in Table 6.
In the ilngdp and igfcf equations, the error correction term (ECT) coefficients are negative and statistically significant at the 5% and 10% levels, respectively.
They are -0.
This shows that there is at least one long-term causal relationship.
The ECT coefficients' comparatively small magnitudes point to a gradual rate of adjustment toward long-term equilibrium.
Table 7.
Error Correction Model Source: Processed Data, 2025 ECTt-1 Coefficient of ilnfin Coefficient of ilngdp Diagnostic Tests F-serial LM F-ARCH test F-White Het.
JB normal ilngdp 2308 .
ilngfcf -01495 .
ilnfin 0325 .
Note: Significant at: *10, **5 percent levels The idea that Islamic financial development may have a short-term positive impact on business activity and economic growth is further supported by the positive, albeit statistically insignificant, coefficients of ilnfin in the ilngdp and igfcf equations.
In the meantime, the ilngdp coefficient in the ilnfin equation is likewise positive but statistically insignificant, suggesting a possible inverse relationship in which the growth of the economy could support the expansion of the Islamic banking industry.
The ECM was subjected to a number of diagnostic tests in order to confirm the traditional assumptions of the OLS model.
At the 5% and 1% significance levels, the results in the ilngdp and igfcf models do not indicate serial correlation, heteroskedasticity, or ARCH effects.
Nonetheless, autocorrelation is present according to the ilnfin model (F-serial LM = 2.
7958, significant at the 10% leve.
The residuals are confirmed to be normally distributed by all models passing the Jarque-Bera normality test at the 5% level (Table .
Annual International Conference on Islamic Economics (AICIE) | 140 Haryani Santo Hartono.
Rininta Nurrachmi CUSUM 5% Significance Figure 4.
CUSUM Result Source: Processed Data, 2025 To assess whether the estimated ARDL model retains structural stability over the observation period, the CUSUM (Cumulative Sum of Recursive Residual.
and CUSUM of Squares (CUSUMSQ) tests are applied as part of the stability diagnostics at the end of this study.
The consistency of model parameters over time is one of the fundamental presumptions of the ARDL framework, which makes these tests crucial.
The overall reliability of the study findings may be jeopardized if the model is structurally unstable, as this calls into question the validity of both the short-run and long-run estimates.
CUSUM of Squares 5% Significance Figure 5.
CUSUMSQ Result Source: Processed Data, 2025 According to the results of the CUSUM test, the cumulative sum of the recursive residuals is within the 5% significance bounds, indicating that the parameters of the model remained Annual International Conference on Islamic Economics (AICIE) | 141 Haryani Santo Hartono.
Rininta Nurrachmi constant over the course of the sample.
This demonstrates that the estimated coefficients are not impacted by any notable structural breaks.
But according to the CUSUMSQ graph, the line just barely touches and crosses the upper 5% boundary.
indicating a possible instability in the residuals' variance at a particular moment in time.
This suggests the potential for brief shocks or variations in the error term throughout the study period, albeit not a serious one.
Although the model is thought to be stable enough for inference overall, care should be taken when interpreting the findings, particularly for long-term projections.
CONCLUSION
The article analysed the dynamic interaction between growth of Islamic finance and the economy in Indonesia by using quarterly data between the year 2015 to 2024 based on ARDL and ECM methods.
The results show that Islamic bank financing has a great/ positive impact on GDP and capital formation in the long-term corroborating the hypothesis of ample supply and showed no support to the fact that Islamic bank financing has an ample demand.
This suggests the fact that the causality is not reciprocal because it is Islamic finance which triggers economic In spite of that, their short-term effect is not statistically significant, and the range of transition to long-run equilibrium is a gradual process.
Stability tests ensure that the model is quite strong although it experiences slight variations throughout the sample period.
Such findings indicate a strategic position into ensuring that Islamic finance is reinforced to ensure that inclusive and sustainable growth is extended to Indonesia.
The policymakers should aim to raise the market share of the sector, which is now less 5 percent by establishing friendly policies plus building institutional capacity and utilizing digital processes besides legal reforms, such as special dispute resolution processes.
Islamic finance can play a maximum role in the national development when it is related to a stable macroeconomic environment.
Although this research topic relates to Indonesia, and assumes the use of a restricted number of macroeconomic factors, prospects of this type of study are to increase the scope by the cross-country comparison, the structural break models, and the complement variations like the financial inclusion.
Shariah governance, and the quality of Also, sector wise contribution such as SME and microfinance financing might give more information on the long run on the picture of Islamic finance and growth of the economy.
REFERENCES