127 IJIBEC DETERMINANTS OF NON-PERFORMING FINANCING OF MORTGAGE IN ISLAMIC COMMERCIAL BANKS Rofadatul Hasanah1, Dina Fitrisia Septiarini2, and Dian Filianti3 1,2,3 Islamic Economics, Faculty of Economics and Business, Universitas Airlangga Airlangga st. Num.4 Correspondence email: rofadatul.hasanah-2016@feb.uniar.ac.id Abstract This study aims to determine the effect of Capital Adequacy Ratio, Return on Assets, BI 7-Day Rate, and Inflation towards Non-Performing Financing Mortgages in Islamic commercial banks in Indonesia. The population of this study is the Islamic commercial banks in the period 2015-2019. The sample used is a saturated sample, which uses all Islamic banks as research samples. This research uses a quantitative approach using time series data. All variables use the percentage of growth and show the results of the level stationary so that the technique used is Ordinary Least Square (OLS) regression analysis which is processed using E-Views 10 software. The results of this study indicate partially the Capital Adequacy Ratio and Return on Assets variables have a negative influence significant to Non-Performing Financing Mortgages. While BI 7-Day Rate and Inflation variables do not influence Non- Performing Financing Mortgages. Nonetheless, Capital Adequacy Ratio, Return on Assets, BI 7-Day Rate, and Inflation simultaneously have a significant effect on the Non-Performing Financing of Mortgages in Islamic commercial banks in Indonesia in the period 2015-2019. International Journal of Islamic Business and Economics Available at http://e-journal.iainpekalongan.ac.id/index.php/IJIBEC ISSN 2599-3216 E-ISSN 2615-420X Vol 4 No 2 2020 Keywords: Microeconomics; Macroeconomics; Non Performing Financing; Home Ownership Loan DOI: https://doi.org/10.28918/ijibec.v 4i2.2725 JEL: G 21, G 32 Article Info Article History: Received : 19 Juli 2020 Accepted : 22 Dec 2020 Published : Dec 2020 mailto:rofadatul.hasanah-2016@feb.uniar.ac.id https://doi.org/10.28918/ijibec.v4i2.2725 https://doi.org/10.28918/ijibec.v4i2.2725 128 International Journal of Islamic Business and Economics (IJIBEC), 4(2) December 2020, 127-137 1. Introduction The level of housing demand is 800,000 units per year while housing needs that can be met until the end of 2017 are around 400,000 units to 500,000 units per year. This makes the price of land and house prices very expensive, in addition to the less available land makes the housing supply gap (backlog) reached 13.5 million units in 2017 (Indonesian Cabinet Secretary, 2017). The government through the Ministry of Public Works and Public Housing (PUPR), continues to provide alternative policies for granting Housing Loans/Mortgages (KPR) through various programs that have been running to help low-income people who cannot buy houses in cash. The Ministry of Public Works and Housing has appointed both the conventional and Islamic banking industry and the non-bank financial industry (IKNB) as KPR distributors (Public Communications Bureau, 2019). Muamalah in cash or mortgages, which is the banking party needs a clear agreement regarding the scheme that will be used according to Haris (2007), one of which is iB Murabaha, istishna’ and ijarah especially ijarah muntahiyah bittamlik (IMBT). Besides, there is a new scheme, namely Musharaka Mutanaqisah (Financial Service Authority, 2016). Increased Islamic Banking mortgages financing (iB KPR) because this product is one of the primary needs that are currently in high demand by the public. According to the 2019 Sharia Banking Statistics (SPS) data, the portion of KPR financing distribution in 2015-2019 tends to increase every month following figure 1. Figure 1. Chart of Distribution of Mortgage Financing Portions (Billion Rp), 2015 – 2019 Source: Sharia Banking Statistics Data 2016-2018, processed. Risks to mortgages are reflected in the ratio of Non-Performing Financing (NPF) or problem loans. The lower the NPF, the lower the level of problem loans that occur, and the better the condition of the bank. A high NPF value indicates that the Islamic bank has low bank health and must bear a large risk as well. Therefore, Islamic banking must be selective in channeling credit to customers. Non- Performing Financing Mortgage Ratio from 2015-2019 in Figure 1.2 tends to decrease. Non-Performing Financing Mortgage Ratio in the Islamic commercial bank industry Mortage Financing Portions International Journal of Islamic Business and Economics (IJIBEC), 4(2) December 2020, 127-137 129 listed in Figure 1.2 experienced a significant increase of 4.53% in 2016, then in May 2016 decreased by a significant enough so that the value of the NPF KPR ratio of 2.76%. The Non- Performing Financing Mortgage Ratio in Figure 1.2 is fluctuating but tends to decrease. The decline in KPR NPF is a wise step by the central bank (Bank Indonesia) or Islamic banking in addressing and dealing with the occurrence of problematic financing that is maintained well from sharing factors that can influence it. Some studies show factors that have a significant effect on NPF in Islamic banks, such as Asnaini's research (2017), namely SBIS and CAR variables; Auliani and Syaichu (2016), namely BOPO, CAR, Inflation, and SBIS variables; Firdaus (2015) namely CAR and GDP; Akbar (2016) namely GDP, CAR, and FDR; Yusof, et al (2018) variable interest rates affect sharia housing loans; Haifa and Dedi (2015) namely FDR, profit-loss sharing, inflation, and exchange rates; Lidyah (2016), namely CAR, BI rate, and BOPO. Of several above research, the variables used in this study are the Capital Adequacy Ratio (CAR), Return on Assets (ROA) BI 7-Day Rate or Interest Rate, Inflates. Based on a series of problem descriptions above, this study is interested in researching on "Analysis of Factors Affecting Non-Performing Financing of Housing Loans at Islamic Commercial Banks from 2015 to 2019". According to Muhammad (2009: 91), types of financing by Islamic banks according to the nature of its use is divided into productive financing and consumer financing. Various types of productive and consumptive financing are facilitated by Islamic banking, one of which is financing for residential homes on credit or called KPR iB financing. Bank Indonesia defines a Housing Loan/Mortgages (KPR) as a credit facility provided by banks to individual customers who will buy or repair a house. In providing credit financing, Islamic banks are required to conduct a feasibility analysis of the customer. This is intended to find out that the customer is truly trustworthy and can pay his debts. Credit financing assessment criteria that must be considered according to Kasmir (2009: 115) namely 5 C + 1 S which consists of an assessment of the character of the prospective recipient (Character), subjective assessment of the recipient's ability to make payments (Capacity), capital capabilities of the recipient (Capital), collateral owned by the recipient (recipient), economic conditions of the recipient (Condition), and the business or goods provided do not violate sharia following the DSN (Sharia) fatwa. Dendawijaya (2005) defines NPF as the failure of the debtor to fulfill its obligations to pay the installments (principal) of the agreed principal. Non-Performing Financing (NPF) is the most crucial thing in the Islamic banking system, because of this relating to bank liquidity and profitability. By Muhammad's research (2005: 359), namely problem financing increases, the risk of decreasing profitability is greater. If profitability decreases, the bank's ability to expand financing decreases, and the pace of financing decreases. Asnaini's research (2017) in Mardiani (2013) explains that the increasing CAR ratio of Islamic banks, Islamic Commercial Bank will feel safe to channel financing. The results of previous studies by research Akbar (2016); Auliani and Syaichu (2016); Lidyah (2016). Asnaini (2017) have significant and negative effects on NPF. Capital Adequacy Ratio is often called the capital ratio authorized capital that must be met by the bank. Under Bank Indonesia regulation No 3/21/PBI/2001 that banks are required to provide a minimum capital of 8% of the weighted assets according to the risk stated in the Capital Adequacy Ratio. Capital Adequacy Ratio (CAR) defined by Dendawijaya (2009) is a ratio 130 International Journal of Islamic Business and Economics (IJIBEC), 4(2) December 2020, 127-137 that shows how far all bank assets that contain risks (loans, investments, securities, bills at other banks) are also funded from the bank's capital funds besides obtaining funds from sources outside the bank, such as public funds, loans (debt) and others The level of profitability of the company can be proxied into the ratio of Return On Assets (ROA). Return on Assets according to Wardoyo and Endang (2009) is a ratio to measure a company's ability to make a profit compared to the company's total assets. Bank Indonesia has set a safe standard of ROA ratios ranging from 0.5% to 1.25%According to Yuwono (2012), the basis for achieving high profitability is to fulfill obligations to shareholders, to assess the performance leadership, and to increase the attractiveness of investors to invest their capital. Rresearch that shows ROA has significant and negative influences, namely Wardoyo and Endang Research (2009) on NPLs in rural credit banks and Raysa (2014) on NPF in Islamic commercial banks. Interest rates according to Karl and Fair (2001: 52) are annual interest payments in the form of a percentage of loans obtained and the amount of interest received each year divided by the number of loans. Interest rates in Keynesian theory are positively related to the number of credit offers and vice versa is negatively related to the number of loan requests means that the higher the loan interest rate is reflected the more expensive the cost will reduce credit demand, and vice versa. This phenomenon reflects that the high lending rate is currently one of the considerations of the public in making loans to banks. The benchmark interest rate becomes the reference for Islamic banks in determining the house price margin, the BI 7-Day Rate. Yusof research results (2018), interest rates have a significant and positive effect on Islamic housing loans. Lidyah Research (2016); Hernawati, et al (2018) showed that the BI rate influences NPF. Inflation is an increase in process general prices of goods continuously (Sunariyah, 2004: 17). This does not mean that the prices of various types of goods go up with the same percentage. Some theories that discuss why inflation occurs, according to Nature (2006), namely: First, the Quantity Theory expressed by the opinion of classics that the price level is determined by the amount of money in circulation. The price will rise if there is the additional money supply. Second, Keynes's Theory sees inflation as the level of demand for needs increases while supply remains, what will happen is that prices will rise. Auliani and Syaichu's (2016); Rahmadani (2016) shows that inflation has a significant effect on KPR NPF on Islamic banks. 2. Research Method This study uses a quantitative approach because this study presents data in the form of numbers. According to Kurniawan and Zahra (2016: 18), Quantitative research is structured research and quantifies data to be generalized. This approach and method began by gathering data, analyze data, and interpret it. Secondary data used in this study is time- series data, in the form of monthly financial statement data from the Financial Service Authority and BI. The population in this study is the entire Industry of the Indonesian Sharia Commercial Bank in 2015-2019 which distributed mortgages in Indonesia. The sample in this study uses saturated samples. According to Sugyono (2014: 68), the saturation sampling technique is a sampling technique when all members of the population are used as samples. This was due to limited sources of information from several Islamic commercial banks International Journal of Islamic Business and Economics (IJIBEC), 4(2) December 2020, 127-137 131 related to CAR, ROA, BI 7-Day Rate, Inflation, Non-Performing Financing Mortgages so that the chosen is the entire Islamic commercial bank industry. Parametric test in this study uses the Ordinary Least Square (OLS) regression method or the least-squares method using statistical tools E-Views 10 and Microsoft Excel 2016 The main step in conducting data analysis techniques is to first tabulate the research data needed following the sample research, as well as this regression model must meet several classic assumption test requirements. 3. Results and Discussion Data Stationarity Test (Unit root test) Based on the results of the stationarity test using the Augmented Dickey-Fuller (ADF) test at the level, the results of the probability of all independent variables are less than α = 5%. It can be concluded that stationary data or data is feasible to be tested using the Ordinary Least Square (OLS) method. Classical Assumption Testing following: Normality Test, based on the results of the panel data Multicollinearity test with the help of E- Views 10 application, the results of the Normality test can be seen in the value of the Jarque-Fallow Test (J-B Test) which shows a Jarque-Fallow value of 0.503738 with a probability value of 0.568500 is greater than α = 5%. It can be concluded if the Prob (Jarque- Bera) value is greater α = 5%. that the data is normally distributed so that it meets the requirements in the regression model. Multicollinearity Test, based on the results of the panel data Multicollinearity test with the help of the application E-Views 10, the results of testing the VIF value shows all independent variables produce a VIF value <10. Based on these results, all independent variables have been free from the classic problem of multicollinearity so that this regression model is worth testing. Heterokedasiticity Test, based on the results of the panel data Multicollinearity test with E-Views 10 application assistance, Breusch-Food- Godfrey Test Result shows the Chi- Square probability of 0.9018, more than α = 5%. It can be concluded that there is no heteroscedasticity problem. Autocorrelation Test, based on the test results The method used to test the autocorrelation is the Breusch-Godfrey Serial Correlation LM Test in table 4.6 with lag 5 obtained a DW value of 1,931 which is greater than the du limit (1.7266) and smaller than (2.2734). The du value is obtained from the Durbin Watson table with a significance value of 5% with the independent variable (k) being 4 and observation (n) being 59. It can be concluded that there are no autocorrelation symptoms. CAR growth variable has a significant and negative effect on the growth of KPR NPF. This research is in line with research by Raysa (2014); Akbar (2016); Diansyah (2016); Auliani and Syaichu (2016); Lidyah (2016) Asnaini (2017) which shows the CAR variable has a negative and significant effect on Non- Performing Financing in Islamic banks. This means that the higher the CAR, the bank will be more careful to provide financing, which in turn will reduce the NPF level of Islamic commercial mortgage loans in Indonesia. According to Auliani and Syaichu (2016), the significant result of this ROA is because every bank has criteria and different requirements in providing funding. There is a clear initial agreement between the customer and the bank (contract) for good faith that 132 International Journal of Islamic Business and Economics (IJIBEC), 4(2) December 2020, 127-137 emphasizes the mandate and forms of a business carried out by Islamic banks in overcoming the problem of default. One of the actions is to save non-performing loans by banks such as Rescheduling, Reconditioning, Restructuring, and Execution (Adlan, 2016). The method of execution is the last method such as handing over obligations to the BUPN (State Receivables Agency) and submitting the case to the district court (civil case). One other way banks do to reduce problem loans is by auctioning houses that have failed to pay by a mortgage (KPR) or transferring credit (take-over credit) of the first home buyer to someone else. ROA growth variable has a significant and negative effect on the growth of KPR NPF. This means that the higher the ROA, the level of mortgage financing problems in a bank decreases. This research in line with the study of Wardoyo and Endang (2009) which shows ROA has a significant and negative effect on NPL. High profitability obtained from good management, according to Berger DeYoung's (1997) research in Kusuma and A Mulyo's (2016) research which states that bad management practices will have an impact on corporate profits. When managers do not have credit scoring skills-assessing building up to the supervision of the debtor, this mismanagement will increase bad credit to banks. The high profitability indicates that the bank has a good performance that attracts the public to place their funds in the bank. For trust it is this community that banks can raise a lot of funds which will then be distributed, then the ratio of bad loans can then be suppressed. Conversely, if the profits are bad, the bank may raise the interest rate policy for the sake of achieving profit targets that will only increase the possibility of debtors defaulting. There is a tendency to decrease the NPF KPR ratio because this financing is a consumptive activity in the presence of pawning goods or collateral in the form of the house itself, the collateral can be used if the borrower is unable to pay it off. BI 7-Day Rate growth variable had no significant and negative effect on the growth of KPR NPF. This result is in line with the research of Astuty and Nisa (2018) which shows insignificant results on Islamic mortgage financing. These results indicate that the economic turmoil that makes the interest rate fluctuations or BI 7-Day Rate does not affect the NPF KPR. Covenants on mortgages such as Murabaha, istishna’, IMBT, and musyarakah mutanaqisah in their application do not use interest rates in determining the amount of the installment because the beginning of the agreement has been determined the amount of margin and ratio between the bank and the customer. The Islamic banking system in Indonesia in channeling mortgage financing applying Islamic principles that are free of interest and penalties. Also, Sharia mortgages apply a fixed rate, which means that the mortgage installments will not change from the initial installment to the end of repayment and there is no penalty or administrative fees (provision) if the customer makes early repayments. Inflation growth variable did not have a significant and positive effect on the growth of the NPF KPR, which meant an increase in the inflation rate, so the level of problematic financing in a bank would remain. This research is in line with Asnaini (2017); Syahputra and Achmad (2019); Hernawati, et al (2019). Not significant inflation to the NPF KPR because inflation conditions in the study period tend to be stable a year) and according to the target determined by Bank Indonesia (classified as mild inflation is <10%). Inflation can occur because of people's desire to consume excessively. The customer's decision to make a KPR transaction indicates that the customer feels that he has a responsibility or commitment to International Journal of Islamic Business and Economics (IJIBEC), 4(2) December 2020, 127-137 133 fulfill his obligations in terms of repaying loans to the bank so that even though inflation has increased, problematic financing at Islamic banks has not increased as well. Asnaini's research (2017) shows no the effect of deep inflation installment payments due to installment payments by customers do not increase if inflation rises but remains as large as the initial contract and also because the initial contract and also changes in the rate of inflation do not dampen the desire of the public to follow the development or reduce consumption, the impact of financing risks can still be controlled. According to Fisher in Akbar (2016) states that rising inflation in the short term will not dampen people's desire to follow the fulfillment of needs, then the impact of credit risk in the short term can still be controlled. According to Bank Indonesia (2013), Islamic banking more resistant to macroeconomic variable shocks. This is evident at the time of the recession and the crisis of Islamic banks are more able to survive than conventional banks. This is evidenced by the high growth of financing in 2008/2009. Besides, rising inflation causes the value of the collateral (the house) will also increase. The higher the value of the collateral can cover if the debtor fails to pay. The results of the probability value on the Simultaneous Test (Test F) that have been done that is equal to 0.000054 which is smaller than the value of α (0.05). Then it can be concluded that the growth of each variable CAR, ROA, BI 7-Day Rate, and Inflation together influences the growth of Non-Performing Financing Mortgages so that H0 is rejected. Based on the R-Square value shown by the same table, the value is 0.363812 or 36.38%. This shows that the CAR, ROA, BI 7-Day Rate, and Inflation variables in the regression model can explain the NPF KPR variable in Islamic banks in the 2015-2019 period by 36.38% and the rest is equal to, which means 63.62% is influenced by other variables. Which is not used in this study. 4. Conclusion Capital Adequacy Ratio or capital adequacy ratio has a significant and negative effect on Non-Performing Financing Mortgages. The CAR variable is the most important factor for banks. The greater the value of CAR, the more careful the banking sector is in giving the financing will make the opportunity for NPF KPR smaller. Return On Assets has no significant and negative influence on Non-Performing Financing Mortgages. ROA variable influences increasing or decreasing mortgage NPF. The high level of profit is obtained because in this financing there are goods pledged or collateral, thus encouraging customers to pay on time which will indirectly affect the profitability of banks. Inflation has a significant effect and positive for Non-Performing Financing KPR. In the short term, if inflation increases will not affect the problematic financing of Islamic banks. Rising inflation will not discourage people from continuing to carry out their obligations. Inflation also makes the value of the guarantee has increased, so it can cover problem financing if the debtor defaults. The suggestions of this study are as follows, banks must implement a prudent system when channelling capital to customers and further develop professional banking performance from the Islamic banking system so that it can improve bank profitability to minimize the occurrence of Non-Performing Financing Mortgages. 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Diponegoro University. http://presidenri.go.id/program-prioritas-2/rumah-subsidi-untuk-masyarakat-penguduk-rendah.html http://presidenri.go.id/program-prioritas-2/rumah-subsidi-untuk-masyarakat-penguduk-rendah.html 127 Null Hypothesis: G_NPF has a unit root Exogenous: Constant Lag Length: 1 (Automatic - based on SIC, maxlag=10) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -9.671679 0.0000 Test critical values: 1% level -3.550396 5% level -2.913549 10% level -2.594521 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(G_NPF) Method: Least Squares Date: 02/16/20 Time: 19:24 Sample (adjusted): 3 59 Included observations: 57 after adjustments Variable Coefficient Std. Error t-Statistic Prob. G_NPF(-1) -2.058990 0.212889 -9.671679 0.0000 D(G_NPF(-1)) 0.385547 0.123004 3.134432 0.0028 C 0.271611 1.052230 0.258129 0.7973 R-squared 0.782778 Mean dependent var -0.008070 Adjusted R-squared 0.774732 S.D. dependent var 16.72466 S.E. of regression 7.937916 Akaike info criterion 7.032375 Sum squared resid 3402.568 Schwarz criterion 7.139904 Log likelihood -197.4227 Hannan-Quinn criter. 7.074164 F-statistic 97.29666 Durbin-Watson stat 1.972534 Prob(F-statistic) 0.000000 Null Hypothesis: G_CAR has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=10) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -10.13383 0.0000 Test critical values: 1% level -3.548208 5% level -2.912631 10% level -2.594027 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(G_CAR) Method: Least Squares Date: 02/16/20 Time: 19:24 Sample (adjusted): 2 59 Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. G_CAR(-1) -1.210712 0.119472 -10.13383 0.0000 C 0.799552 0.425538 1.878921 0.0655 R-squared 0.647121 Mean dependent var 0.168103 Adjusted R-squared 0.640819 S.D. dependent var 5.349206 S.E. of regression 3.205869 Akaike info criterion 5.201718 Sum squared resid 575.5454 Schwarz criterion 5.272767 Log likelihood -148.8498 Hannan-Quinn criter. 5.229393 F-statistic 102.6945 Durbin-Watson stat 2.054270 Prob(F-statistic) 0.000000 Null Hypothesis: ROA has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=10) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -9.885397 0.0000 Test critical values: 1% level -3.548208 5% level -2.912631 10% level -2.594027 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(ROA) Method: Least Squares Date: 02/16/20 Time: 10:26 Sample (adjusted): 2 59 Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. ROA(-1) -1.238849 0.125321 -9.885397 0.0000 C 10.64678 6.985303 1.524168 0.1331 R-squared 0.635704 Mean dependent var -1.955517 Adjusted R-squared 0.629199 S.D. dependent var 85.89595 S.E. of regression 52.30503 Akaike info criterion 10.78594 Sum squared resid 153205.7 Schwarz criterion 10.85699 Log likelihood -310.7922 Hannan-Quinn criter. 10.81361 F-statistic 97.72107 Durbin-Watson stat 2.078909 Prob(F-statistic) 0.000000 Null Hypothesis: G_BI7_DAY has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=10) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.982016 0.0001 Test critical values: 1% level -3.548208 5% level -2.912631 10% level -2.594027 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(G_BI7_DAY) Method: Least Squares Date: 02/16/20 Time: 19:26 Sample (adjusted): 2 59 Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. G_BI7_DAY(-1) -0.614213 0.123286 -4.982016 0.0000 C -0.414170 0.487595 -0.849414 0.3993 R-squared 0.307106 Mean dependent var -3.06E-17 Adjusted R-squared 0.294733 S.D. dependent var 4.357031 S.E. of regression 3.659042 Akaike info criterion 5.466154 Sum squared resid 749.7610 Schwarz criterion 5.537204 Log likelihood -156.5185 Hannan-Quinn criter. 5.493829 F-statistic 24.82048 Durbin-Watson stat 2.012591 Prob(F-statistic) 0.000006 Null Hypothesis: G_INFLASI has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=10) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -6.965870 0.0000 Test critical values: 1% level -3.548208 5% level -2.912631 10% level -2.594027 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(G_INFLASI) Method: Least Squares Date: 02/16/20 Time: 19:25 Sample (adjusted): 2 59 Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. G_INFLASI(-1) -0.907481 0.130275 -6.965870 0.0000 C -0.834967 1.308484 -0.638118 0.5260 R-squared 0.464234 Mean dependent var 0.217241 Adjusted R-squared 0.454667 S.D. dependent var 13.40413 S.E. of regression 9.898498 Akaike info criterion 7.456517 Sum squared resid 5486.895 Schwarz criterion 7.527567 Log likelihood -214.2390 Hannan-Quinn criter. 7.484193 F-statistic 48.52334 Durbin-Watson stat 1.990382 Prob(F-statistic) 0.000000 Appendix Statisionary Test Normality Test 0 2 4 6 8 10 12 14 -25 -20 -15 -10 -5 0 5 10 15 Series: Residuals Sample 1 59 Observations 59 Mean 7.00e-16 Median -0.857662 Maximum 17.45295 Minimum -22.80573 Std. Dev. 7.701291 Skewness -0.003741 Kurtosis 3.480831 Jarque-Bera 0.568500 Probability 0.752578 Multikoliniearity Test Variance Inflation Factors Date: 02/16/20 Time: 19:16 Sample: 1 59 Included obs ervations : 59 Coefficient Uncentered Centered Variable Variance VIF VIF G_CAR 0.094205 1.088256 1.065758 G_ROA 0.000380 1.075098 1.039752 G_BI7_DAY 0.074049 1.054575 1.024440 G_INFLASI 0.011810 1.087926 1.071907 C 1.227165 1.136564 NA Heterokedastisity Test Heteroskedasticity Test: Breusch-Pagan-Godfrey Null hypothesis: Homoskedasticity F-statistic 0.245036 Prob. F(4,54) 0.9114 Obs*R-squared 1.051806 Prob. Chi-Square(4) 0.9018 Scaled explained SS 1.092915 Prob. Chi-Square(4) 0.8954 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 02/16/20 Time: 19:18 Sample: 1 59 Included observations: 59 Variable Coefficient Std. Error t-Statistic Prob. C 53.80009 13.20370 4.074624 0.0002 G_CAR 2.266985 3.658306 0.619682 0.5381 G_ROA 0.149283 0.232366 0.642450 0.5233 G_BI7_DAY -1.492958 3.243426 -0.460303 0.6471 G_INFLASI -0.717082 1.295286 -0.553609 0.5821 R-squared 0.017827 Mean dependent var 58.30463 Adjusted R-squared -0.054926 S.D. dependent var 92.62188 S.E. of regression 95.13157 Akaike info criterion 12.02934 Sum squared resid 488700.9 Schwarz criterion 12.20540 Log likelihood -349.8654 Hannan-Quinn criter. 12.09806 F-statistic 0.245036 Durbin-Watson stat 1.663144 Prob(F-statistic) 0.911436 128 International Journal of Islamic Business and Economics (IJIBEC), 4(2) December 2020, 127-137 Autokorelation Test Breusch-Godfrey Serial Correlation LM Test: Null hypothesis: No serial correlation at up to 3 lags F-statistic 4.170882 Prob. F(3,53) 0.0100 Obs*R-squared 11.26876 Prob. Chi-Square(3) 0.0104 Test Equation: Dependent Variable: RESID Method: Least Squares Date: 02/14/20 Time: 11:13 Sample: 1 59 Included observations: 59 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. CAR -0.027212 0.280666 -0.096955 0.9231 ROA 0.012798 0.018468 0.692971 0.4914 C -0.098757 0.987729 -0.099984 0.9207 RESID(-1) -0.491521 0.139446 -3.524814 0.0009 RESID(-2) -0.188950 0.151656 -1.245911 0.2183 RESID(-3) -0.053591 0.138324 -0.387431 0.7000 R-squared 0.190996 Mean dependent var -3.31E-16 Adjusted R-squared 0.114675 S.D. dependent var 7.816682 S.E. of regression 7.354850 Akaike info criterion 6.924741 Sum squared resid 2866.973 Schwarz criterion 7.136016 Log likelihood -198.2799 Hannan-Quinn criter. 7.007215 F-statistic 2.502529 Durbin-Watson stat 1.994396 Prob(F-statistic) 0.041627 Regesion OLS Result Dependent Variable: G_NPF Method: Least Squares Date: 02/16/20 Time: 19:15 Sample: 1 59 Included observations: 59 Variable Coefficient Std. Error t-Statistic Prob. G_CAR -0.881640 0.306928 -2.872470 0.0058 G_ROA -0.095536 0.019495 -4.900507 0.0000 G_BI7_DAY -0.131332 0.272120 -0.482624 0.6313 G_INFLASI 0.125897 0.108673 1.158492 0.2518 C 1.779146 1.107775 1.606054 0.1141 R-squared 0.363812 Mean dependent var 0.308814 Adjusted R-squared 0.316687 S.D. dependent var 9.655408 S.E. of regression 7.981429 Akaike info criterion 7.073050 Sum squared resid 3439.973 Schwarz criterion 7.249113 Log likelihood -203.6550 Hannan-Quinn criter. 7.141778 F-statistic 7.720129 Durbin-Watson stat 2.687802 Prob(F-statistic) 0.000054