(Luận văn) the role of credit and monetary transmission in vietnam, a var approach

t to ng UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM hi INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS ep w n lo ad VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS ju y th yi pl n ua al n va THE ROLE OF CREDIT AND MONETARY TRANSMISSION IN VIETNAM: A VAR APPROACH ll fu oi m at nh z z ht vb BY k jm NGUYỄN LÊ THẢO NGUYÊN om l.c gm n a Lu MASTER OF ARTS IN DEVELOPMENT ECONOMICS n va y te re th HO CHI MINH CITY, May 2012 t to ng hi ep UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS w n lo ad VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS ju y th yi pl n ua al va n THE ROLE OF CREDIT AND MONETARY TRANSMISSION IN VIETNAM: A VAR APPROACH ll fu oi m at nh z z A thesis submitted in partial fulfilment of the requirements for the degree of k jm ht vb MASTER OF ARTS IN DEVELOPMENT ECONOMICS om n a Lu NGUYỄN LÊ THẢO NGUYÊN l.c gm By n y te re Dr NGUYỄN VĂN NGÃI va Academic Supervisor: th HO CHI MINH CITY, May 2012 t to ACKNOWLEDGEMENT ng hi ep I have deeply grateful to many people who support and advise me during the writing w this thesis The thesis will not be complete without their assistance and encouragement n lo My first thanks to my supervisor- Dr Nguyen Van Ngai, who gave me valuable ideas, ad comments, suggestions, and motivation during the preparation of this thesis Thanks to y th yi study ju his friendly attitude and enthusiasm has given me more self-confident to complete this pl ua al I sincerely thank Tutor- Mr Phung Thanh Binh who provided me materials, also economic techniques relevant to my thesis I am grateful to all lecturers and tutors of n ll fu invaluable contributions n va Vietnam-Netherlands Master Program in Development Economic, who have made oi m I wish my express my special thanks to my friends Nguyen Van Dung, Le Anh Khang, Vo Thi Ngoc Trinh They have spent a lot of time and effort in helping me during my at nh writing z z Last but not least, I thankful to my lover, parents and brother who were always beside vb me and gave me spiritual support k jm ht om l.c gm n a Lu n va y te re th t to ng hi ABSTRACT ep w Among different channels (namely: interest rate, asset price, credit, exchange rate n lo channel) are affected by monetary policy, which one plays as a key channel in this ad mechanism In this study, I investigate the role of credit channel in monetary y th ju transmission mechanism in the case of Vietnam Two different specifications of loan yi markets are conducted: classical market (without domestic credit) and augmented pl ua al market (with domestic credit) to look for the evidences of the role of credit Vector autoregression model which focuses on the reduced form will be employed as main n n va econometric techniques in this thesis The empirical results support that credit channel ll fu plays important role in monetary transmission in Vietnam case The correlation oi m between lending channel and monetary policy is somewhat weak The policy implication that credit sector should be carefully controlled when implements new at nh monetary policy z z Key words: domestic credit, credit channel, monetary policy transmission, VAR vb model k jm ht om l.c gm n a Lu n va y te re th t to ng hi LIST OF TABLES ep Table 2.1: One decade and Vietnam’s credit w Table 3.1: The data sources n lo Table 4.1: Description of variables ad Table 4.2: Augment Dickey-Fuller test y th ju Table 4.3: Philips- Perron test yi Table 4.4: Optimal lap-Classical market pl ua al Table 4.5: Optimal lap- Augmented market Table 4.6: VAR Regression Statistic- Classical market n n va Table 4.7: VAR Regression Statistic- Augmented market ll fu Table 4.8: Variance Decompositions for vector autoregression for Classical and oi m Augmented Market at nh z z LIST OF FIGURES vb Figure 3.1: Analytical Framework ht k jm Figure 4.1: The impulse response functions for classical market om l.c gm Figure 4.2: The impulse response functions for augmented market n a Lu n va y te re th t to ng hi LIST OF ABBREVIATIONS ep IFS-IMF: International Financial Statistic- International Monetary Funds w SBV: State Bank of Vietnam n lo OMOs: Open Market Operation ad SOCBs: State Owned Commercial Bank y th ju JSBs: Join Stock Bank yi ADF: Augmented Dickey Fuller pl ua al PP: Philips-Perron LR: sequential modified LR test statistic n n va FPE: Final prediction error oi m SC: Schawarz information criterion ll fu AIC: Akaike information criterion HQ: Hannan-Quinn information criterion nh at U.S: United States z z k jm ht vb om l.c gm n a Lu n va y te re th t to TABLE OF CONTENTS ng hi ep CHAPTER 1: INTRODUCTION w 1.1 RELEVANCE AND BACKGROUND OF STUDY n lo 1.2 PROBLEM STATEMENT 10 ad 1.4.RESEARCH QUESTION 11 y th 1.6 STRUCTURE OF THESIS 12 ju yi CHAPTER 2: LITERATURE REVIEW 14 pl 2.1 CREDIT CHANNEL THEORY 14 al n ua 2.2 MONETARY POLICY FRAMEWORK OF VIETNAM 16 n va 2.2.1 LEGAL FRAMEWORK 16 fu 2.2.2 MONETARY POLICY STRATEGY AND INSTRUMENTS 17 ll 2.2.3 VIETNAM’S FINANCIAL MARKET OVERVIEW 19 m oi 2.3 EMPIRICAL LITERATURE 21 nh at 3.1 ANALYTICAL FRAMEWORK 27 z 3.2 MODEL SPECIFICATION 28 z ht vb 3.3 DATA SOURCES 30 jm 3.4 STEPS OF ESTIMATION 32 k CHAPTER 4: FINDING AND DISCUSSION 34 gm 4.1 DESCRIPTIVE STATISTIC 34 om l.c 4.2 UNIT ROOT TESTS 35 a Lu 4.3 VAR REGRESSION STATISTICS FOR CLASSICAL AND AUGMENTED MARKET 37 n 4.4 IMPULSE RESPONES AND VARIANCE DECOMPOSITIONS 40 va n 4.5 RESULTS COMPARISON 47 th y 5.1 CONCLUSIONS 49 te re CHAPTER 5: CONCLUSION AND POLICY IMPLICATION 49 t to 5.2 POLICY IMPLICATION 50 ng hi 5.3 LIMITATION AND FURTHER STUDIES 53 ep 5.3.1 LIMITATION 53 w 5.3.2 FURTHER STUDIES 53 n lo REFERENCES 55 ad APPENDIX 58 ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th t to CHAPTER 1: INTRODUCTION ng hi ep This chapter will present how important of this study is, its objectives and research questions In addition, a brief of methodology is also mentioned in this chapter Finally, w the scope and structure of thesis are deal with in this part n lo ad ju y th 1.1 RELEVANCE AND BACKGROUND OF STUDY yi The restructuring of state owned commercial banks (SOCBs) and the establishment of pl join stock banks (JSBs), had appeared since Vietnam financial reform in the first haft al n ua of 1990s Consequently, the finance system of Vietnam has deepened when va monetization increased continuously (in 2004, the ratio M2 to GDP was above n estimated 70% compare to 25% in mid-1990) Seventy-three percentage of total credit fu ll is provided by SOCBs in 2004 The credit market and other parts of financial system to m oi be segmented proceeding JSBs and others small banks supplied credit primarily to nh at private sector, whereas SOCBs almost loaned both sectors equivalently (Camen, 2006) z z ht vb When Vietnam took part in the World Trade Organization (WTO), lead to the surge of jm new foreign direct investment and portfolio inflows Globalization, it’s synonymous k with Vietnam has posed significant challenges to their economy Unfavorable balance gm of payments is also the major concern Vietnam’s financial sector has been explosion om l.c since 2000 year only, noticeable in 2007-2008 periods Consequently, Vietnam’s credit market grew too hot, the number was estimated about 50 percentage in January 2008, a Lu that contributed a positive element to inflation rising, got 14 percentage at this time n va (Ishii, 2008) And at early months of year 2011, Vietnam’s inflation rate accelerated to n 13.89 percent in March, peak out at highest in 25 months Moreover, the trade gap y te re increased to $1.15 billion that month after look over $1.11 billion in February (S&P th Reporting, 2011) t to 1.2 PROBLEM STATEMENT ng hi ep For facileness, most of economic models usually assume that the changes of economy which affect by financial conditions have just relatively bounded by set of several w n financial variables They could be risk-free interest rates in short term or government lo ad bond rates in long term (Hall, 2001) y th ju However, once the system of financial develops with high degree, especially in recent yi pl years, its impact on the economy becomes wider and deeper Hence, it’s quite hard to ua al find the root of problem when the economy is developed, because of some variables n may not be indicated For example, the world financial crisis in 2008 had a root from va n credit sector, typically, mortgage assets crisis in the U.S or the refugee capital of fu ll Vietnam security market in 2008 due to the easing monetary policy in previous years m oi In the past, many economists such as Pintinkin, Gurley, Shaw, etc., emphasized the at nh important role of financial intermediaries and credit markets Modigliani and z Papademos (1977) also admitted that the traditional theory of monetary mechanism z vb ignored the functions of financial intermediaries and bank credits Financial jm ht intermediaries were strong influence on credit supply than money supply (Gurley and Shaw, 1956) Evidently, credit channel contributes a significant factor and affects k gm directly to decisions of policy makers l.c Hence, understanding the position of credit channel in financial market is crucial to om policy makers In detail, understanding the transmitted mechanism of monetary policy a Lu through credit channel is very important As a result, indentify the role of credit n channel in monetary transmission is essential for enhancement current policies By that n va way, it contributes to the achievement of national economic objective y te re th 10 t to while it is blured in classical model Although lending rate shock has improved its role ng hi in augmented market, but still somewhat weak in comparison with others factors The ep output response is modestly smaller in the market with credit (8.4 percent compare to w 9.2 percent at nine quarters) n lo Another point of the result output variance decomposition, M2 shock accounts smaller ad in the market with credit (19.9 percent in comparison with 5.1 percent after nine y th ju quarters), and the different becomes larger at longer horizons Augmented market, yi which augments credit variable, makes lending rate drop its contribution from 20 pl ua al percent to 14 percent to variance decomposition in output after five quarters Credit shocks account for 18.9 percent at thirteen quarters, a largest share in forecast error of n ll fu continuously n va output Once again, credit shock proves its important role in forecasting output error oi m According to the results, only output and price level keep relative role in forecast the error of credit, though, that result is somewhat fragile (15.2 and 14.8 percent at 13- nh at quarters horizon respectively) Similar with impulse response results, M2 shock also z z lacks its contribution in predicting credit error, only accounts 3.5 percent after thirteen vb quarters Credit shocks always account nearly haft in forecast error of itself during ht k jm thirteen quarters Beside, with credit variable appearance, lending rate decomposition gm alters most of remain shocks Typically, 30 percent of the error in predicting lending rate is attributed to credit shocks, however, smaller with longer horizon om l.c n a Lu n va y te re th 45 n lo ad ju y th yi pl n ua al Table 4.8: Variance Decompositions for vector autoregression for Classical and Augmented Market Classical Market Dependent Variable Augmented Market Dependent Variable Cl_lr Cl_out put Cl_refi n 6.8 6.8 6.9 8.5 8.0 8.2 3.3 6.7 7.4 12.6 25.4 25.1 oi Cl_cr edit Cl_cpi at nh 68.8 53.0 52.4 Cl_m m ll z Cl_credit decomposition at 13 Cl_c pi 100 fu Cl_cpi decomposition at n va Quarter s z 13 Cl_lr 100 Cl_credi t ht vb Cl_m Cl_ou tput Cl_r efin 66.5 52.2 51.5 1.9 2.4 3.3 6.6 10.7 11.4 6.9 7.1 7.1 3.9 9.5 9.2 14.1 18.1 17.5 3.9 96.1 0 0 14.8 14.9 14.8 55.9 44.9 43.2 9.7 12.4 12.4 3.2 2.9 3.5 13.3 15.2 15.2 3.1 9.6 10.9 6.6 37.5 32.2 34.3 30 12.1 9.9 9.3 63.4 27.5 26.6 22.9 6.2 15.0 14.7 4.6 4.8 7.7 12.2 11.5 11.1 1.2 27 22.2 20.9 58.1 26.1 21.3 21.5 2.9 7.8 11.2 10.8 37.7 21.2 16.8 15.7 6.2 8.4 10.3 11.5 20.0 20.8 1.3 12.6 3.2 87.8 18.1 19.0 18.9 14 12.7 11.9 6.2 5.1 6.0 45.9 34.3 33.9 6.8 9.0 10.9 19.6 0 71.5 21.6 21.3 19.7 6.5 10.7 10.9 3.9 6.6 8.7 19.6 22.4 20.8 87.7 40.5 35.9 32.9 5.4 10.5 10.9 4.8 5.5 6.9 Cl_m2 decomposition at 13 2.3 21.5 19.3 18.9 0.4 4.8 7.5 7.7 97.3 63.3 56.0 54.6 9.0 9.2 10.1 1.4 7.9 8.7 Cl_output decomposition at 0.9 11.6 1.2 86.2 0.2 13 6.6 18.1 19.2 20 16.2 14.8 20 19.9 20.8 47.1 36.3 35.2 6.3 9.3 9.9 8.9 19.9 18.4 11.4 12.7 0.4 75.4 8.5 0.4 13 54.8 44.3 43.2 13.9 12.3 12.2 11 14.2 14.6 2.8 5.9 6.8 17.5 23.3 23.2 47.4 37.9 36.9 0.9 1.2 2.8 k jm om l.c gm an Lu va n Cl_refin decomposition at 6.3 10.8 11.5 ac th 12.3 43.1 37.3 37.7 y te 13 re Cl_lr decomposition at si Sources: Calculated form IMF-IFS and GSO data eg cd 46 jg hg t to In brief, we can collect some remarkable concludes from empirical results In VAR ng hi regression statistic, neither the lagged value lending rate nor refinancing rate help ep predict money supply, hence, classical market might be lacked something Meanwhile, w credit is highly significant in predict money supply in augmented market In impulse n lo response and variance decomposition, the correlation between money supply and ad lending rate shock is sometimes abnormal in market without credit Whereas M2 y th ju reactions immediately and strongly for credit shock Credit shock plays important role yi in forecasting the error of money supply There have varied in magnitude, also order pl ua al among macro variables with credit appearance in augmented market Credit and price level master a lot of output and M2 n n va ll fu 4.5 RESULTS COMPARISON oi m Thesis’s results comparison will compare above estimation findings with other at nh research relevant in Vietnam, also in other countries Hence, we can point out the z similarity and difference among them to indentify the thesis’s contribution z vb Our finding confirms the existing of credit channel and its role in Vietnam case since jm ht Bernanle and Gertler (1995) research The empirical results in this study consistent with majority previous study using VAR model as a main approach such as Kim k gm (1999), Lown and Morgan (2002), Disyatat and Vongsinsirikul (2003), Abdul (2009); l.c and structure vector autoregression model such as Kubo.A (2007), Catão and Pagan om (2010) that credit channel plays important role in monetary transmission mechanism a Lu Once again, this study is in line with Podpiera (2007), Charoenseang J and Manakit P n (2006) results by reconfirmed the role of credit channel in the case of Thailand or Chile va n in despite of different countries and econometric techniques With similarly topic for y te re Vietnam research, the study agrees with Hung and Pfau (2008) and conclusion that th 47 t to credit channel is more important than interest rate channel and to be the most ng hi significant channel in the case of Vietnam ep However, the study’s finding conflicts with several researches, such as Ramey (1993), w Suzuki (2004) when their empirical provides evidence the insignificant role of credit in n lo transmission of monetary policy ad In general, despite of different market conditions and Vietnam’s specific characteristic, y th ju the empirical finding still has same results to majority relevant studies yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th 48 t to ng CHAPTER 5: CONCLUSION AND POLICY IMPLICATION hi ep This chapter will give conclusion and base on research findings, then we express policy w implication also raise some recommendations Finally, limitation and further research n lo of thesis are shown ad y th 5.1 CONCLUSIONS ju The thesis has identified the role of credit in monetary transmission mechanism in yi pl Vietnam case over 1996:Q1 to 2010:Q3 period Take advantage form IFS-IMF sources, ua al most of data in this study that are extracted from here, excepting industrial output This n study uses VAR model with reduced form as optimal economic techniques to answer va n key question Two markets are specified, including classical (without credit) and ll fu augmented market (with credit) that aim to explore the role of credit channel m Stationary and unit-root test is the first step to test which data can be employed to run oi each case before VAR estimated at nh our model And base on minimum AIC criteria, we can choose the optimal lags for Then, VAR Granger-causality test is used to z z examine causality between money supply and credit variable, also some relevant vb jm ht factors Impulse response is employed to see the reaction of each variable that shocks occurring Finally, variance decomposition is used to test how percentage of each k gm variable is contributed to forecast error of monetary shock at given horizon The l.c estimated results are following: om First, there is no existing independent variables is Granger causality with money supply a Lu in classical market Money supply helps to predict most of dependent variable in n market without credit, excepting the lending rate and vice versa Therefore, VAR va n regression statistic for classical market might be not sufficient to explain monetary 49 th lending rate are another ones help to predict money supply while they lost this function y significant to predict money supply And the worth findings that price level and te re transmission mechanism In augmented market, the domestic credit is highly t to in classical market But, the lagged value of money supply does not help to forecast ng hi credit, also output or lending rate Obviously, with augment credit variable in ep augmented market has contributed significance to accept the important role of credit w channel in monetary policy transmission n lo Second, in classical market, the output and refinancing rate response to monetary ad policy shock (tightening monetary policy in case) after one lag The output responses y th ju quite sensitive to money shock whereas the reaction of lending channel to money shock yi short affected and abnormal sometimes For market with credit, the reaction of output pl ua al to credit shock is robust, happening after one lag, whereas the response of M2 is immediately, strongly and consistent with literature of Bernanke and Gertler The n n va correlation between lending channel and credit channel is fairly weak ll fu Third, there is a discriminated between two markets in variance decomposition of M2 oi m More than fifty percent of the error in the forecast of M2 is attributed by credit shocks at first quarter in augmented market Credit and price level shocks master a lot of nh at variance decomposition of M2 and diminishing at larger horizons There has a big z z difference in magnitude, also the order of variance decomposition of M2 in market vb with credit ht k jm Since those findings, the study agrees with Bernanke and Gertler view that credit gm channel played as important channel in monetary transmission mechanism in Vietnam case om l.c a Lu 5.2 POLICY IMPLICATION n The study reveals some important implications for policy maker concerning to credit va n channel and monetary transmission mechanism As the estimation results of thesis y te re proved the important role of credit channel in monetary policy transmission in th 50 t to Vietnam; hence; in order to regulate the economy development through reasonable ng hi monetary policy at each period, some recommendations are given below: ep Firstly, credit channel plays important role in monetary transmission mechanism in the w case of Vietnam Therefore, credit sector need to be carefully controlled when n lo implements the new monetary policy Simply, it is a channel which be directly affected ad and strongly if government changes from loosen to tighten monetary policy and vice y th ju versa SBV plays crucial role to regulate credit flow to the economy by its instruments yi SBV as a central bank that manages and monitors the operation of commercial banks, pl ua al need to make appropriate recommendations and practical policies to Government To carry out these tasks more effectively, should gives autonomy to SBV in decision n n va making ll fu Secondly, money-quasi responses to credit shock immediately and strongly and output oi m also falls dramatically to credit shock after one lag only Once tightening monetary policy is implemented; according to theory, this action will affect directly to credit nh at channel through two sub channels: bank lending channel and balance sheet channel z z All of them lead to one resulting, that is output downward The main cause comes from vb the shortage in credit supply for manufacturing enterprises Therefore, it is essential to ht k jm having support programs for these enterprises By that way, we can prevent too hot gm economic growth but not influence to production sector, especially an agriculture country like Vietnam In recent years, many subsidized programs have been l.c om implemented to this sector by the Vietnam’s government However, there are many Government need control and supervise to ensure the policy effectively n a Lu insufficient and outstanding in implementation those programs Hence, the n va Thirdly, the relationship between lending channel and monetary policy does not follow 51 th control in credit business, in which interest negotiation However, the commercial y under develop of Vietnam’s finance market is one of Although, has been given a self- te re as theoretical mechanism sometimes The causes have rooted from many reasons, t to banks still depend on the guidance of SBV in making lending decisions Hence, it is ng hi essential having another solution for lending rate rather than reality, a gradual ep marketization lending mechanism w Fourthly, rely on variance decomposition result; credit and price level matter a lot of n lo decomposition of money-quasi and diminishing at larger horizons Other words, credit ad growth and inflation play crucial role for error of money quasi in short-run y th ju Correspondingly, credit growth should be carefully control Essential for finance yi development projects instead of speculation operation to prevent bubble or bad debts pl ua al Go further, monetary policy should be implemented cautiously and flexibly in order to enhance the effectiveness of money stock control In order to meet this duty, once n n va again, the SBV need supervise, and forecast the changes in domestic as well as global ll fu financial market to impose appropriate policies timely oi m Once, we understand the importance of credit channel in monetary transmission mechanism clearly We can eliminate the bad effects on it due to new policy nh at implemented And if those measures are implemented successfully, they will be z significant improvement the health of Vietnam’s financial market z k jm ht vb om l.c gm n a Lu n va y te re th 52 t to ng hi ep 5.3 LIMITATION AND FURTHER STUDIES 5.3.1 LIMITATION Although, this study has answered all the key questions about the role of credit in w monetary transmission mechanism in Vietnam case, it also contains some limitations n lo In particular, the availability of data resources is very limited for Vietnam country Due ad y th to this reason, the study can not access longer period of time Additionally, Vietnam’s ju GDP value only has existed since 2000 year, so that the thesis must use industrial yi output value as a proxy for GDP Again, standard variable; as proxy of the net pl ua al percentage tightening6 had employed in Lown and Morgan’s research Then, domestic n credit value is used instead of it va Even VARs model are a powerful tools for indentify the role of credit in monetary n ll fu transmission However, it has made lasting contributions to macro econometricians’s at nh (Stock and Watson, 2001) oi m toolkit for describing data and generating reliable multivariable benchmark forecast z z 5.3.2 FURTHER STUDIES vb jm ht This study has employed impulse response and variance decomposition of VAR model to examine the role of credit and monetary transmission for quarterly data 1996:Q1 to k gm 2010:Q3 A further study could use monthly data for longer period to see the correlation, also reaction between them more exactly An addition, the further study l.c om should employ different model such as vector error correction (VECM) model to Besides that, the study could add several variables such as Federal Funds rate, n a Lu consider whether above conclusion is still effective in the case of Vietnam va exchange rate to exploit how the changes of estimated result with joined new variables n y te re The number of banks tightening less the number easing, divided by the number reporting (Lown and Morgan, 2002) 53 th t to The credit plays important role in monetary transmission Hence, potential study may ng hi expand by consider the determinants of domestic credit in Vietnam case ep w n lo ad ju y th 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The economic record, vol 80, no 249: 145–156.(June) VietnamPlus News (2009) WB predicts growth of 5.5 percent Available from http://www.ncseif.gov.vn/sites/en/Pages/wbpredictsgrowthof5.5percent-nd13529.html Warner.E.J and Georges C (2001) The credit channel of monetary policy transmission: Evidence from stock returns Economic Inquiry Vol 39, Issue Available at SSRN: http://ssrn.com/abstract=253088 World Bank report,(2006) Overview the capital markets in Vietnam and directions for development Banking Finance and Investment Book-651, (May) w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th 57 t to ng hi ep APPENDIX Original data illustration w Figure 1a n lo 2,400,000 ad ju y th 2,000,000 yi 1,600,000 pl ua al 1,200,000 n 800,000 va n 400,000 fu ll 1998 2000 2004 2006 OUTPUT 2008 2010 CREDIT at nh M2 2002 oi m 1996 z Figure 1b z jm ht vb 200 160 k gm 120 l.c om 80 a Lu 40 n 1998 2000 2004 REFIN 2006 CPI 2008 2010 y te re LR 2002 n 1996 va th 58 t to ng Table 3: Descriptive statistic of level data hi ep w Mean Median Maximum Minimum Std Dev Skewness Kurtosis n lo ad ju y th Jarque-Bera Probability CREDIT 549177.7 265074.0 2350413 46248.30 617670.3 1.440890 4.044569 LR 12.11701 11.17500 21.00000 8.516667 3.094763 1.418351 4.485826 M2 592226.1 314276.3 2252240 47197.23 613318.5 1.213169 3.303865 OUTPUT 91988.86 78923.00 208347.0 27417.00 52686.84 0.512347 1.989271 REFIN 8.060734 6.500000 18.90000 4.800000 3.952356 1.469578 4.419529 12.15698 0.002292 23.09796 0.000010 25.20912 0.000003 14.69947 0.000643 5.092613 0.078371 26.19035 0.000002 32401487 2.21E+13 714.9036 555.4984 34941342 2.18E+13 5427343 1.61E+11 475.5833 906.0249 59 59 59 59 59 CL_LR -0.323911 0.000000 35.05047 -32.24432 9.643404 -0.259913 8.077671 CL_M2 6.983427 6.579830 35.56684 -0.048531 4.702351 3.912347 24.71221 CL_OUTPUT 3.654520 4.089613 12.42440 -8.978253 4.453939 -0.638997 3.804989 CL_REFIN -0.520797 0.000000 50.00000 -42.85714 13.69579 0.420237 7.485213 5.513088 0.063511 50.32354 0.000000 211.9621 1130.742 -30.20625 10691.76 58 58 yi CPI 99.60786 85.71913 166.5833 70.38970 28.70622 1.110776 2.900381 5876.864 47794.72 Observations 59 pl Sum Sum Sq Dev n ua al va n Table 4: Descriptive statistic of changed data ll fu Jarque-Bera Probability 48.49465 0.000000 1076.092 0.000000 62.96132 0.000000 Sum Sum Sq Dev 87.52489 232.1594 413.3026 1634.482 -18.78682 5300.729 405.0388 1260.390 Observations 58 58 58 58 oi at nh z z vb k jm ht 1287.227 0.000000 gm om l.c CL_CREDIT 7.125907 6.837460 38.95502 -0.319156 5.354916 3.715985 22.74957 m CL_CPI 1.509050 1.214760 8.968614 -1.535087 2.018160 1.432867 6.443011 Mean Median Maximum Minimum Std Dev Skewness Kurtosis n a Lu n va y te re th 59