Jurnal Ekonomi & Studi Pembangunan Volume 19, Nomor 2, Oktober 2018, hlm. 157-166 DOI: 10.18196/jesp.19.2.5007 THE DETERMINANT OF INFLATION IN INDONESIA: PARTIAL ADJUSTMENT MODEL APPROACH Yosefina Don Sama Lelo, Rini Dwi Astuti, Sri Suharsih Universitas Pembangunan Nasional Veteran Yogyakarta Jl. SWK 104, Condongcatur, Depok, Sleman, Daerah Istimewa Yogyakarta 55283 Correspondence E-mail: rinidwiastuti@upnyk.ac.id Received: August 2018; Accepted: October 2018 Abstract: Inflation is one of the economic issues that always being targeted by the government, par- ticularly central bank because it could adversely influence the economy. For the past view years, the inflation targeting framework as the part of monetary policy has been successfully implemented where the interest rate is the operational target. In view of past investigations, there are fundamental factors that affect inflation, for example, interest rate, exchange rate, and money supply. This study aims to evaluate the impact of those factors on inflation both in the short and long run. The estimation uses monthly data from January 2013 to November 2017, which was obtained from Indonesian Banking Statistics. The use of Partial Adjustment Model illustrates how interest rates, exchange rate, and money supply negatively and significantly affect inflation on both short and long run. This regression result is consistent with the finding of previous studies which strengthen the evidence that the government should maintain the inflation rate through those variables. Keywords: Inflation, Monetary Policy, PAM JEL Classification: E31, E52, C22 INTRODUCTION Inflation is an economic problem which could affect the negative impact on a country economic. Thus, inflation is often target in government policy. High inflation will affect negatively the economy because it leads to such unrest condition, high unemployment, and slow economic growth. All in all, those will result in low economic growth. (Suparmoko, 1992). Theoretically, inflation is a condition in which the increasing price of goods and services continuously in a certain period. If the process doesn’t occur at the same time but with the same percentage, it doesn’t call as inflation (Nopirin, 1987). Monetary authority published the mone- tary policy to anticipate the high inflation rate or to decide the macro policy. Monetary policy can be done by interest rate, open market policy, cash ratio, or foreign exchange policy (Mizaroh, 2014). Table 1. Inflation Rate in Indonesia from 2008-2016 Year Inflation Target Realization 2013 4.5% 8.38% 2014 4.5% 8.36% 2015 4.0% 3.35% 2016 4.0% 3.02% 2017 4.0% 3.30% Source: Bureau Labour of Statistics, 2013 Based on table 1, the growth of the inflation rate can be seen to reach the highest rate in 2013 with 8,38%, much below the government target at 4.5%. The main reason was that the fuel price which increased to Rp6.500/litre for premium and Rp5.500/litre for solar. It affects the inflation for 1,17%. The increasing price of subsidized fuel affects to the other prices such as transportation within cities. The transporta- tion gives 1,75%, red onion 0,38%, electricity 0,38%, red pepper 1,31%, fish 0,3%, rice 0,2%, cigarettes 0,19%, airfare 0,19%, workers 0,16%, home assistant wage 0,1% (LPI, 2014). mailto:rinidwiastuti@upnyk.ac.id 158 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 157-166 In 2014, the inflation rate is 8,38%. This was because of the pressure of the price from the previous year. In 2015 onward the inflation rate can be handled below the government target. Central Bank of Indonesia as the mone- tary authority that holds the monetary policy to handle the national economy is the one that decides money flow with interest rate. Interest rate affects the individual decision on deciding either to spend or to save money in deposit (Suhaedi, 2000). Externally, when rupiah appreciates to- ward USD can be caused by the government external debt or private external debt. In result, the exported goods become much cheaper. The cheap price effects the increasing volume of goods. It is related to the demand law when the price is low, the demand will increase. The in- creasing output can reduce the inflation rate and decrease the price. Hendrawan (2016) and Perlambang (2012) state that exchange rate shows the balance between supply and demand toward foreign exchange rate. Rupiah appreci- ation reflects the society demand on rupiah and the increasing demand on forex as an international currency. Rupiah depreciation makes imported goods become much more expensive and exported goods become much cheaper. This condition needs to look at because it leads to inflation. Generally, inflation gives some social price bear by society. First, the income distri- bution will get affected. A low class society with fixed income will bear the condition with their low purchasing power. On the other hand, upper-middle-class society will protect their saving and deposit so their purchasing power still stays the same. Both inflations give a negative impact on the economy. High inflation effects the instability of economic, high unemployment, slow economic growth on the country. On this research, we would explain the 3-month-deposit effect on the conventional bank, exchange rate, and money supply toward inflation in Indonesia from January 2013-November 2017. RESEARCH METHOD Type and Data Source The type of data used in this research is secondary monthly data period January 2013 – November 2017, including: 1. Inflation period January 2013 – November 2017 taken from Indonesia Bureau of Statis- tics on percentage. 2. Interest rate represented by 3-month-de- posit in conventional bank period January 2013 – November 2017 from Statistic of Indonesian Banking on percentage. 3. Rupiah exchange rate on USD from January 2013 – November 2017 in Rupiah. 4. Money supply from January 2013 – Novem- ber 2017 taken from Indonesian Financial Statistic (SEKI). Statistical Test Significance Test The hypothesis that will be tested in this research is related to the significance of independent variables (deposit interest rate, exchange rate, and money supply) toward the dependent variable (inflation) partially or simultaneously. 1. F Test F Test aims to know whether all independent variables tested significantly affected the dependent variable. The test is done through ANOVA test with 95% degree, with the requirements: a. If F test < F table, Ho is not rejected b. If F test >>F table, Ho is rejected 2. t Test Partial hypothesis test aims to know the affect and significance of each independent variable to the dependent variable. This done through t-test with 95% degree, with the requirement: a. H0 : if p-value > 0,05, Ho is not rejected b. H0 : if p-value <0,05, Ho is rejected The Determinant of Inflation: … (Yosefina Don S. Lelo, Rini Dwi Astuti, Sri Suharsih) 159 Adjusted R Square The closer it gets to 0, the less impact of independent variables might give to the dependent variable. However, if it closer to 1, the higher impact of independent variables might give to the dependent variable. Autocorrelation Test The test aims whether there is a disturbing correlation on the multiple linear regressions model on t period with previous t period. If there is a problem, we called it auto- correlation. We can go through Durbin Watson (DW Test). Heteroskedasticity Test This classic test aims to see whether on regression model exist the inconsistence vari- ances from one residue to the other. If there is a problem then we call it as heteroskedasticity. A good model should never be having heteroske- dasticity. We can see from scatterplot from the expected value of Y with residue value where the predictions are scattered. Another way is to do a Part test by comparing t-test and t table. If t-test < t-table then there will be no heteroskedasticity. Multicollinearity Test This aims to know whether there is a correlation among independent variables. A good model should never correlate among each other (Ghozali, 2009). We can go through a variance factor (VIF) test. The prevalent cut off value is used to show multicollinearity is toler- ance value with ≤ 0.10 or the same with VIF ≥ 10 (Ghozali, 2009) Analysis Method In analyzing interest rate, exchange rate, and money supply toward inflation in Indone- sia, we will use Partial Adjustment Model estimation. It is one of the simple models used to estimate the relationship between the independent and dependent variable with lag (Gujarati, 1995). This model assumes the expected de- pendent variable in t period (Yt*) depends on actual independent variables. Written as below: INF = f (SB, NT, JUB) ……………… 3.1 The short-term PAM estimation: INFt = b0 + b1SBt + b2NTt + b3JUBt + b4Yt-1 + e…..……...…3.2 The long-term PAM estimation: Constant = b0/ (1-b4) Coefficient SB = b1/ (1-b4) Coefficient NT = b2/ (1-b4) Coefficient JUB = b3/ (1-b4) Notes: INF = Inflation (%) SB = Interest rate (%) NT = Rupiah Exchange Rate (on Natural Log) JUB = Money Supply M1 (on Natural Log) e = Disturbance Variable RESULT AND DISCUSSION Interest rate fluctuation in Indonesia can be caused by a number of factors, thus it is hard to control inflation. The government should be aware of the initial factors that can form inflation. In Indonesia, inflation is not only a short-term inflation, as said on Keynes’s theory, but also it is a long-term condition (Baasir, 2003). Inflation rate can be reduced or even can be prevented. To reach the inflation rate below government target, all parties need to work all together either from the Central Bank or the private sector. Monetary policy is one of the policies can be done by the government. It aims to bal- ance the internal balance and external balance. Internal balance can be shown by high eco- nomic growth, price stability, and equality de- velopment. While external balance can be shown by the balance of payment, high em- ployment rate, and balance of international payment (Insukindro, 1993). Central Bank of Indonesia using Mone- tary policy to control Rupiah value as the repre- 160 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 157-166 sentative of the stable inflation rate. The main instrument used is BI rate to influence the economic activities with the goal of the inflation rate. To reach one certain inflation rate, the interest rate policy should go through the long transmission. Based on graphic 1, we can see that target inflation can be reached only 3 times. The inflation trend fluctuates because several inflation rates show bad economic activity. The inflation realization can be seen in Figure 1. Figure 1. Inflation Target and Realization Source: Indonesia Banking Statistics 2013-2017 The result from Partial Adjustment Model can be seen at table 2. Table 2. The result of Regression Analysis Variabel Coefficient t-statistic Probabilities C 4.956845 2.302072 0.0253 SB -0.214892 -2.398663 0.0200 NT -0.801375 -2.547670 0.0138 JUB -0.712997 -2.580804 0.0127 Y(t-1) 0.969124 19.01613 0.0000 Adjusted-Squared 0.869362 F-statistic 95.82999 Probabilities (F-statistic) 0.000000 Source: Attachment 1 Based on table 2, the short term PAM model equation is at the below: Y= 4.9568- 0.2148SB – 0.8013NT – 0.7129JUB + 0.9691Y(t-1) Thus, the long term equation is : Y= 16.475 – 6.9614SB – 25.9320NT – 23.0711JUB Statistic Test t-Test and F-Test t-test aims to know whether independent variables partially has significant impat to dependent variable. T-test by using α=5%, df=n-k= 59-4 = 55 is 1.671. If t-statistic < t-table Ho is accepted, and ig t-statistic > t-table Ho is rejected. F-test aims to know whether generally the model can be trusted with certain degree. F-test is used to simultaneously know the affect of interest rate, exchange rate and money supply on inflation. Because F-test is (95.82999)>F-table (2.76) and significancy value 0.000000<0.05, thus Ho is rejected and Ha is accepted, so all variables are affected inflation. Test on Adjusted R2 Coefficient R2 = 0.869362 or 86% means the fluctua- tion on inflation in Indonesia can be desctibed by interest rate, exchange rate, and money supply. The rest of 14% can be described by other factors not in the model. Classical Assumption Test Classical assumption test aims to know the problem of autocorrelation, heterokedasticity or multicollonearity in the model. Because if the model can’t pass the test, f-test and t-test is invalid and the final result is rejected. Normality Test The test is done to know the residu from the estimation is normally distributed. Based on regression result, the Jarque_Bere probability value is 0.10 > probabilitas statistik (α = 5%), so it is normally distributed Table 3. Normality Test Jarque_Bere Value 4.485210 Probability 0.106182 Source: Attachment 2 The Determinant of Inflation: … (Yosefina Don S. Lelo, Rini Dwi Astuti, Sri Suharsih) 161 Autocorrelation Test Autocorrelation test is the comparison between the value of Obs*R-squared with the value of Chi Square table. If Obs*R-squared < value of Chi Square table, there is no autocor- relation existed and vice versa. According to the estimation result, Obs*R-squared 5.192133 < value of Chi Square table 7.815 so there is no autocorrelation. The result is on the table 4. Table 4. Langrange Multiplier Test (LM) Obs*R-squared 5.192133 Probability 0.0746 Source: Attachment 3 Heteroskedastisity Test Table 5 shows the value of Obs*R-squared and White Heteroskedasticity is 0.782230 and Chi Square table df (k-1 = 4-1=3) with α=5% os 7.815. If Obs*R-squared is 0.884633 < value of Chi Square tabel 7,815 so there is no heteroskedasticity exist on the model. See the result on table 5. Table 5. White Heteroskedasticity Test Obs*R-squared 0.884633 Probability 0.9268 Source : Attachment 4 Linearity Test Linearity test can be done to detect the empirical model whether a new variable applies is relevant with the empirical model. Based on the result Ftest is 2.07 < value of Ftable is 2.76. So the empirical model is a linear function. F-table = (α= 0.05 : k-1; n - k) = (α= 0.05 : 4-1; 59 - 4) = (α= 0.05 : 3; 55) (2.76). Tabel 6. Linearity Test F-Statistik 2.073192 Probability 0.1363 Source : Attachment 5 Multicollinearity Test The test result is on the below: Table 7. Multicollinearity Test R2 INF 0,8785 R12 SB 0,0663 R22 NT 0,0616 R32 JUB 0,0183 Source: Attachment 6 Table 8 shows R-Squared from the PAM estimation> R-Squared value of interest rate, exchange rate and money supply so there is no multicollinearity exists. Based on the hypothesis test, we can con- clude that interest rate has negative affect on inflation. The regression coefficient in short term is 0.21. When interest rate increases by 1 %, inflation decreases by 0.21% in short term. In long term, regression coefficient is -6.95%. When interest increases by 1%, inflation de- creases by 6.95%. This is linked with the hy- pothesis because during January 2013 – November 2017, the interest rate is one of the main reasons why people save or deposit their money in bank. This is in tune with the result from the previous research by Rahmawati (2011). Exchange rate has negative affect on inflation. The regression coefficient is -0.80. It means, if exchange rate increases by 1%, inflation will decrease to 0.80% in short term. While in long term, the regression coefficient is -25.93%. In other words, when exchange rate increases by 1%, the inflation rate will decrease by 25.93%. On January 2013-November 2017, when rupiah depreciates in USD, so the imported goods become much more expensive and exported goods become much cheaper. It is in the contrary with the research from Nugroho, et.al (2012) states that exchange rate does not influence on inflation. 162 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 157-166 This matches with the research from Fadel (2013) proves that exchange rate influence inflation rate positively during 1981–2011. The depreciation of Rupiah makes inflation rate higher, and vice versa. This implicates the theory from parity purchasing power when domestic currency is related positively with the domestic inflation and foreign currency. So, the government should proactively make strategic decision to strengthen its currency to reduce inflation. Money supply has negative relationship on inflation. The regression coefficient in short term is -0.71% .This shows when money supply increases by 1%, inflation will decrease by 0.71%. In long term, however, the regression coefficient is -23.07%. This is not what the hy- pothesis stated in first place. This can be caused money supply that hold by society is not only for consumptive buying but also for productive buying. The increasing money supply leads the real sector to produce goods and services exceeding the demand so can reduce the price. This is the same with the research by Nugroho, et al. (2012) where high money supply will not sufficient enough to influence inflation. CONCLUSION Based on the analysis from the previous chapters, the effect of interest rate, exchange rate and money supply in Indonesia from 2013 – 2017 can be described below : 1. Interest rate in short and long term has negative affect on inflation. The high inter- est rate will be responded by the society by saving or depositing their money in bank. 2. Exchange rate in short and long term has negative affect on inflation. This is because the exchange rate depreciation cause high production cost. 3. Money supply in short and long term has negative affect on inflation. This is because people tend to buy on productive goods. REFERENCES Baasir, F. (2003). Pembangunan dan Crisis. Ja- karta: Pustaka Harapan. Badan Pusat Statistik Indonesia. Data Inflasi ta- hun 2008-2017. (Online) website: bps.co.id. Bank Indonesia. Laporan Bulanan 2008-2017. (Online) website: www.bi.go.id. Fadel. (2013). Analisis Pengaruh PDB, Suku Bunga Deposito, dan Nilai Tukar Terhadap Inflasi di Indonesia Periode 1981-2011. Thesis. Unpublish. Ghozali, I. (2009). Aplikasi Analisis Multivariate Dengan Program SPSS. Semarang: Badan Penerbit Universitas Diponegoro. Gujarati. N.D. (2003). 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Pengaruh Pengeluaran Pemerintah dan Jumlah Uang Beredar https://rizalhendrawankd.wordpress.com/2016/11/18/01/ https://rizalhendrawankd.wordpress.com/2016/11/18/01/ https://rizalhendrawankd.wordpress.com/2016/11/18/01/ The Determinant of Inflation: … (Yosefina Don S. Lelo, Rini Dwi Astuti, Sri Suharsih) 163 terhadap Inflasi. (Online) Wordpress Ekonomi Pembangunan. http://mizaroh.wordpress.com/ekono mi-pembangunan/97-2014. Nopirin. (1987). Ekonomi Moneter. 4th Ed. Yog- yakarta: BPFE. Nugroho, P.W.B., and Maruto, U. (2012). Ana- lisis Faktor-Faktor Yang Mempengaruhi Inflasi Di Indonesia Periode 2000–2011 Jurnal of Accounting, Universitas Diponegoro, 1(1). Perlambang, H. (2012). Analisis Pengaruh Jumlah Uang Beredar, Suku Bunga Sbi, Nilai Tukar Terhadap Tingkat Inflasi. Media Ekonomi, 19 (2) Rahmawati. (2011). Pengaruh Jumlah Uang Beredar, Pengeluaran Pemerintah, dan Suku Bunga terhadap Tingkat Inflasi di Nanggroe Aceh Darussalam. Jurnal Ap- likasi Manajemen. 9(1). Suhaedi, et al. (2000). Suku Bunga Sebagai Salah Satu Indikator Ekspetasi Inflasi. Buletin Ekonomi Moneter dan Perbankan, March 2000. Suparmoko. (1992). Ekonomi Pembangunan. 5th Ed. Yogyakarta: BPFE UGM. ATTACHMENT Attachment 1 Partial Adjustment Model (PAM) Dependent Variable: INF Method: Least Squares Date: 18/03/18 Time: 12:17 Sample (adjusted): 2013M02 2017M11 Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 4.956845 2.153210 2.302072 0.0253 SB -0.214892 0.089588 -2.398663 0.0200 NT -0.801375 0.314552 -2.547670 0.0138 JUB -0.712997 0.276269 -2.580804 0.0127 INF(-1) 0.969124 0.050963 19.01613 0.0000 R-squared 0.878530 Mean dependent var 5.464138 Adjusted R-squared 0.869362 S.D. dependent var 1.851307 S.E. of regression 0.669134 Akaike info criterion 2.116599 Sum squared resid 23.73026 Schwarz criterion 2.294223 Log likelihood -56.38136 Hannan-Quinn criter. 2.185787 F-statistic 95.82999 Durbin-Watson stat 1.456726 Prob(F-statistic) 0.000000 Attachment 2 Normality Test 0 1 2 3 4 5 6 7 8 -2 -1 0 1 2 3 Series: Residuals Sample 2013M01 2017M11 Observations 59 Mean -2.52e-15 Median -0.648934 Maximum 3.537607 Minimum -2.631770 Std. Dev. 1.793471 Skewness 0.415367 Kurtosis 1.934931 Jarque-Bera 4.485210 Probability 0.106182 164 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 157-166 Attachment 3 Autocorrelation Test Breusch-Godfrey Serial Correlation LM Test: F-statistic 2.507190 Prob. F(2,51) 0.0915 Obs*R-squared 5.192133 Prob. Chi-Square(2) 0.0746 Test Equation: Dependent Variable: RESID Method: Least Squares Date: 18/03/18 Time: 12:20 Sample: 2013M02 2017M11 Included observations: 58 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C 0.914678 2.190552 0.417556 0.6780 SB 0.015121 0.087624 0.172571 0.8637 NT 0.058795 0.307102 0.191451 0.8489 JUB -0.100064 0.279145 -0.358467 0.7215 INF(-1) -0.036262 0.057155 -0.634444 0.5286 RESID(-1) 0.329759 0.147626 2.233745 0.0299 RESID(-2) -0.060107 0.150858 -0.398432 0.6920 R-squared 0.089520 Mean dependent var -7.12E-16 Adjusted R-squared -0.017596 S.D. dependent var 0.645229 S.E. of regression 0.650881 Akaike info criterion 2.091781 Sum squared resid 21.60593 Schwarz criterion 2.340456 Log likelihood -53.66166 Hannan-Quinn criter. 2.188645 F-statistic 0.835730 Durbin-Watson stat 2.011340 Prob(F-statistic) 0.548096 Attachment 4 Heteroskedasticity Test Heteroskedasticity Test: White F-statistic 0.205223 Prob. F(4,53) 0.9344 Obs*R-squared 0.884633 Prob. Chi-Square(4) 0.9268 Scaled explained SS 2.052931 Prob. Chi-Square(4) 0.7260 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 18/03/18 Time: 12:22 Sample: 2013M02 2017M11 Included observations: 58 Variable Coefficient Std. Error t-Statistic Prob. C 1.614740 1.618741 0.997528 0.3230 SB^2 0.001529 0.008276 0.184748 0.8541 NT^2 -0.026698 0.146754 -0.181923 0.8563 JUB^2 -0.022800 0.027366 -0.833149 0.4085 INF(-1)^2 -0.001028 0.006674 -0.154065 0.8781 R-squared 0.015252 Mean dependent var 0.409142 Adjusted R-squared -0.059068 S.D. dependent var 0.973024 S.E. of regression 1.001350 Akaike info criterion 2.922837 Sum squared resid 53.14315 Schwarz criterion 3.100461 Log likelihood -79.76228 Hannan-Quinn criter. 2.992025 F-statistic 0.205223 Durbin-Watson stat 1.596788 Prob(F-statistic) 0.934408 The Determinant of Inflation: … (Yosefina Don S. Lelo, Rini Dwi Astuti, Sri Suharsih) 165 Attachment 5 Linearity Test Ramsey RESET Test: F-statistic 2.073192 Prob. F(2,51) 0.1363 Log likelihood ratio 4.533602 Prob. Chi-Square(2) 0.1036 Test Equation: Dependent Variable: INF Method: Least Squares Date: 25/05/18 Time: 11:46 Sample: 2013M02 2017M11 Included observations: 58 Variable Coefficient Std. Error t-Statistic Prob. C -4.036025 6.791992 -0.594233 0.5550 SB 0.361528 0.456854 0.791342 0.4324 NT 1.666435 1.869836 0.891220 0.3770 JUB 1.404019 1.586489 0.884985 0.3803 INF(-1) -2.008959 2.160715 -0.929766 0.3569 FITTED^2 0.590539 0.390656 1.511662 0.1368 FITTED^3 -0.035530 0.021838 -1.626982 0.1099 R-squared 0.887663 Mean dependent var 5.464138 Adjusted R-squared 0.874447 S.D. dependent var 1.851307 S.E. of regression 0.655983 Akaike info criterion 2.107399 Sum squared resid 21.94601 Schwarz criterion 2.356073 Log likelihood -54.11456 Hannan-Quinn criter. 2.204262 F-statistic 67.16500 Durbin-Watson stat 1.272383 Prob(F-statistic) 0.000000 Attachment 6 Multicolleniarity Test Multicolleniarity Test SB Dependent Variable: SB Method: Least Squares Date: 18/03/18 Time: 12:24 Sample (adjusted): 2013M02 2017M11 Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 7.185508 3.121091 2.302243 0.0252 NT -0.406991 0.474577 -0.857586 0.3949 JUB -0.071178 0.419535 -0.169658 0.8659 INF(-1) 0.140837 0.075002 1.877771 0.0658 R-squared 0.066378 Mean dependent var 8.472931 Adjusted R-squared 0.014510 S.D. dependent var 1.023856 S.E. of regression 1.016401 Akaike info criterion 2.936884 Sum squared resid 55.78579 Schwarz criterion 3.078984 Log likelihood -81.16964 Hannan-Quinn criter. 2.992235 F-statistic 1.279749 Durbin-Watson stat 0.268492 Prob(F-statistic) 0.290611 166 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 157-166 Multicolleniarity Test NT Dependent Variable: NT Method: Least Squares Date: 18/03/18 Time: 12:25 Sample (adjusted): 2013M02 2017M11 Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C -1.717013 0.901750 -1.904091 0.0622 SB -0.033014 0.038497 -0.857586 0.3949 JUB -0.096614 0.118795 -0.813281 0.4196 INF(-1) 0.033725 0.021565 1.563868 0.1237 R-squared 0.061676 Mean dependent var -2.486724 Adjusted R-squared 0.009547 S.D. dependent var 0.290875 S.E. of regression 0.289483 Akaike info criterion 0.425034 Sum squared resid 4.525233 Schwarz criterion 0.567134 Log likelihood -8.325992 Hannan-Quinn criter. 0.480385 F-statistic 1.183143 Durbin-Watson stat 1.790620 Prob(F-statistic) 0.324796 Multicolleniarity Test Money Supply Dependent Variable: JUB Method: Least Squares Date: 18/03/18 Time: 12:26 Sample (adjusted): 2013M02 2017M11 Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 6.788260 0.521112 13.02648 0.0000 SB -0.007485 0.044117 -0.169658 0.8659 NT -0.125245 0.154000 -0.813281 0.4196 INF(-1) -0.009067 0.025073 -0.361608 0.7191 R-squared 0.018316 Mean dependent var 6.986552 Adjusted R-squared -0.036222 S.D. dependent var 0.323785 S.E. of regression 0.329597 Akaike info criterion 0.684583 Sum squared resid 5.866262 Schwarz criterion 0.826682 Log likelihood -15.85290 Hannan-Quinn criter. 0.739933 F-statistic 0.335831 Durbin-Watson stat 2.076906 Prob(F-statistic) 0.799469