Macroeconomic, financial and institutional determinants of banking crisis the money market pressure index approach

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MACROECONOMIC, FINANCIAL AND INSTITUTIONAL DETERMINANTS OF BANKING CRISIS: THE MONEY MARKET PRESSURE INDEX APPROACH A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By CHAU THE VINH Academic Supervisor: Assoc Prof NGUYEN TRONG HOAI HO CHI MINH CITY, December2014 CERTIFICATION “I certify that the substance of this thesis has not already been submitted for any degree and has not been currently submitted for any other degree I certify that to the best of my knowledge and help received in preparing this thesis and all used sources have acknowledged in this dissertation” CHAU THE VINH st Date: 31 December 2014 i ACKNOWLEDGEMENT Upon completing this thesis, I have received a great deal of encouragement and support from many people First of all, I would like to express my deepest gratitude towards Assoc Prof Nguyen Trong Hoai, my esteemed academic supervisor, for his patient guidance, encouragement and valuable critiques for my research work Also, I would like to thank Dr Truong Dang Thuy for his guidance and advice in econometric techniques, Dr Pham Khanh Nam for his encouragement and valuable advice in the starting phase of my thesis research design My gratefulness is also extended to all of my lecturers and staffs of the VietnamNetherlands Program for their assistance during my first days in this programme Besides, I would love to thank my parents and my families for their ceaseless encouragement and support during my study period Moreover, my special thanks to my C.E.O – Mr Nguyen Huu Tram, who understands and gives me approval for my long personal leave to finalize my thesis on time Without them, I would not have opportunities and incentives to have my thesis finished Finally, I would like to thank all my friends and other people who have had any help and support for my thesis but are not above-mentioned ii ABSTRACT The thesis estimates a logit regression model by fixed effect with a combination of some macroeconomic and financial indicators from the work of Hagen and Ho (2007) and Worldwide Governance Indicators (WGI) from the updated database of Kaufmann (2013) as explanatory variables for binary dependent variable banking crises generated from the approach of money market pressure index (Hagen and Ho, 2007) The monthly panel dataset, which is available in full range and easy of approach from International Financial Statistics CD-ROM (2011), of 18 countries from Latin America and Asian over the scope of 2001 – 2010is applied Some specific lag lengths of indicators are also applied according to the suggestion of “flexibility in forecast horizon” of Drehmann et al (2011) The crisis phenomenon of banking system seems to be well-described in light of the present of depreciation, former year crisis, high real interest rate in prior of 36 months, growth of credit to GDP in prior 12 months Moreover, impact of inflation seems to support the school of thought that it is negative effect to crisis Simultaneously, growth rate of bank deposits to GDP is likely useful to prevent banking systems from profitability risks exposure that leads to banking crisis probability However, unfortunately, the indicators of growth of monetary base and growth of M2 to reserves give incorrect expected sign and negligible effect on banking crisis Furthermore, the included institutional variables from WGI give insignificant statistic meaning Hence, another set of institutional indicators such as that from International Country Risk Guide (ICRG) should be considered in future analysis to test for the relationship between Government health and banking crisis probability Despite, on one hand, there should be a more adequate research to be examined in the future, this thesis attempts to contribute so-called new updates information on the would-be banking crisis determinants Nevertheless, on the other hand, there is likely no proper explanation on the tranquil periods of banking system Hence, it is suggested that thereshould be some assessment ofsuch time of banking system, which over a long time has beenneglected (Kauko, 2014) Key words: banking crisis, tranquiltime, determinants, institutional indicators, fixed effect logitregression TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Problem statement 1.2 Research objective 1.3 Research question 1.4 Structure of the thesis CHAPTER 2: LITERATURE REVIEW 2.1 Defining banking crisis 2.2 Trends of banking crises researchtogether with crises mechanism 2.2.1 The first trend 2.2.2 The second trend 10 2.2.3 The third trend 14 2.3 Money Market Pressure (MMP) Index (Hagen and Ho, 2007) 19 2.4 Chapter summary 21 CHAPTER 3: METHODOLOGY, MODEL SPECIFICATION AND DATA 28 3.1 Model selection 28 3.2 Model specification 31 3.2.1 Macroeconomic indicators 33 3.2.2 Financial indicators 34 3.2.3 Institutional indicators 36 3.2.4 Use of lagged terms 37 3.3 Estimation strategies and relevant model diagnostics 40 3.3.1 Calculation of MMP for banking crisis assessment .40 3.3.2 Model estimation steps and diagnostics 41 3.4 Data scope and sources 43 3.5 Conceptual framework 46 3.6 Research Process 47 CHAPTER 4: RESUTLS AND FINDINGS 48 4.1 Descriptive statistics of explanatory indicators 48 4.2 Statistical tests for model 51 4.2.1 Model specification test 51 4.1.2 Goodness of fit test 51 4.1.3 Test for multicollinearity 51 4.3 Coefficients interpretation 53 4.3.1 Macroeconomic indicators 53 4.3.2 Financial indicators 55 4.3.3 Institutional indicators 57 CHAPTER 5: CONCLUSION, POLICY RECOMMENDATION AND LIMITATION 58 5.1 Conclusion 58 5.2 Policy recommendation 58 5.3 Limitation of the research 60 REFERENCES 61 APPENDICES 65 Table 2.1 Summary of literature reviewed 22 Figure 2.1 Mechanisms of banking crisis 27 Table 3.1 Data for MMP index calculation 44 Table 3.2 Data and sources of explanatory variables 45 Table 4.1 Banking crisis dates retrieved from MMP index 65 Table 4.2 Summary statistics of variables used in the regression 49 Table 4.3a The correlation on the sample observations 50 Table 4.3b The correlation on the sample observations 50 Table 4.4Linktest for specification error of logit model 66 Table 4.5 Goodness of fit test of model 67 Tabel 4.6 Full model multicollinearity test result 67 Table 4.7 Dropping significantly high correlated variables GE, RL .68 Table 4.8 Dropping high correlated variables GE, RL and CC 68 Table 4.9 Using interactive term of GE and RL 69 Table 4.10 Full model 69 Table 4.11 Restricted model without GE, RL, CC 70 Table 4.12 Fixed effect model with lags 70 Table 4.13 Random effect model with lags 71 Table 4.14 Simple logit model with lags 72 Table 4.15Comparison of lagged terms of indicators in simple logit, FEM and REM 73 ABBREVIATION MMP: Money Market Pressure WGI: World Governance Indicator WB: World Bank IMF: International Monetary Fund IFS: International Financial Statistics ICRG: International Country Risks Guide FEM: Fixed Effect Model REM: Random Effect Model BC: Banking Crisis vii CHAPTER 1: INTRODUCTION 1.1 Problem statement Banking crisis in nowadays economies is not a new issue or even an old one that has been given awareness to, discussed and researched from many angles and perspectives by applying many approaches from simple to complicate There have been three trends of banking system crisis researches from its first trend of qualitative description by Friedman and Schwartz (1963) about US crisis over its past decades to the second trend in which econometric analysis with panel data were employed according to relatively enough banking crises observations and to the third trend since the 2007 “global financial turmoil” The trends of banking crisis research contribute most of important indicators related to macroeconomics and banking sectors such as reserves, current account, real exchange rate (Kaminsky et al, 1998) Despite the fact that the logistic regression approach focused more on quantitative economics model, it has seemed to be an important tool for anticipating the crisis signals and timing as well as significant indicators However, there was also some noise that could affect the effectiveness of this model Hence, it led to the rise of further studies in terms of developing new method and other new critical variables As suggested, there have been many criteria to help researchers with banking crisis identification Amongst, money market pressure index from the work of Hagen and Ho (2007), who expanded the literature of Eichengreen, Rose, and Wyplosz (1995, 1996a, 1996b) for currency crisis, stands out to be convenient for understanding and data collecting but still provide good judgment value for banking crisis symptom Such index observed the periods that banking systems experience its liquidity problem by considering simultaneously the phenomenon of both high central bank reserves demand and fluctuations of short-term real interest rate Originally, the index provides the criterion to indicate whether there is a crisis or not under the scope analyzed Banks relevant data, to some extent, seems to be difficult to obtain precisely due to i their sensitiveness Given those difficulties, the research will make use of macroeconomic indicators as suggested in a survey that emphasized “the analysis of macroeconomic variables is of some help for banking supervisors in order to fully assess banks’ health” (Quagliariello, 2008) In accordance with both suggestion from Quagliariello (2008) and Hagen and Ho (2007), some available macroeconomic and financial variables such as inflation, growth of monetary base, depreciation, real interest rate, growth of private credit over GDP, growth of deposits over GDP and growth of M2 over reserves are examined In recent years, there has been the use of institutional signals (Kaufmann et al, 2008) to predict for the probability of vulnerability and crisis occurrence besides quantitative economic indicators to enhance the limitation of the model by Kaminsky et al (1998) Moreover, being motivated by the work of Breuer et al (2006) on institutional variables and currency crisis, this research will take this idea together with the combination with six updated world governance indicators (Kaufmann, 2013) namely voice and accountability, government effectiveness, political stability, rule of law, regulatory quality, control of corruption to assess the role of “health” of Government in the relationship with crisis time of the banking systems Last but not least, the 12-month lagged term of banking crisis included into the regression model (Falcetti and Tudela, 2006) also give significant assessment Nevertheless, it seems that most of relevant researches tend to try to explain the reasons for a banking crisis occurrence but not that why banking crisis does not take place in some situation over some period in some country The attempt to understand or even forecast the crisis is important on one hand But, on the other hand, future researches should be carried out with the tranquil time of the banking system, i.e the “non-crisis” situation, still has its important role which seems to be belittled or even no need to be explained (Kauko, 2014) Although there have been researches and studies on banking crisis, it seems that there are likely few works considering simultaneously the health of Government, macroeconomic and financial background in a same model Thus, the contribution 10 background likely push banking system to the position of exposure to risks and vulnerabilities, which may trigger banking crises Firstly, in terms of macroeconomic indicators, the regression outcome indicates that prior 12-month crisis may be a worrying sign for present banking crisis to occur and/or keep sustained on the basic of prior banking crisis Thus, authorities should pay more attention once confronting a banking crisis in the present time to deploy proper responses from either the standpoint of crisis alleviation or crisis elimination to reduce or even avoid the costly consequences of banking crisis may give bad impacts to output of the economy that may make banking crisis even worse Moreover, in terms of financial indicators, high real interest rate, growth of credit over GDP, growth of deposit over GDP with their lagged terms and depreciation seem to be helpful in banking crisis probability predicting These indicators, more or less, reflect the healthiness of banking system as a whole, thus authorities should take into account their sudden fluctuations to deploy some appropriate interventions One important figure to be considered thoroughly of these indicators is that, their impact may not explicit in real time, yet its accumulations over time, which sometimes are likely to be ignored, may lead to high probability of banking crisis Furthermore, unlike some studies, high inflation in this time of thesis seems to be a good sign, however, it may be due to country scope of the thesis analysis that emerging countries in which inflation may go along with economic growth Inflation is still under debate, hence, cautious observations of inflation indicator are highly recommended In addition, as a matter of fact, there is likelihood that inflation stability program may cause volatility to real interest rate which, in turn, contributes to the gain or loss of bank’s profitability Bad profit of banks tends to put them in a position of highly exposure to banking crisis Hence, inflation control program should be designed carefully and cautiously, particular in harmonizing between program effectiveness and the cost of banking crisis once occurs (Dermirguc-Kunt and Detragiache, 1998) 5.3 Limitation of the research Although there are attempts to have banking crisis determinants indicated, outcomes are still not yet perfect The thesis follows the so-called “old-fashioned” analyses that tend to study the determinants of banking crisis but not that of non-bankingcrisis which is likely important to explain why some countries are exposure to risks while some are not Some criteria for the non-crisis time, or tranquil time, have to be argued and introduced for further studies The combination of factors causing crisis and those can help maintain non-crisis has to be put to consideration to have a more overall view which is critical for authorities in managing the banking system as well as the economy The thesis uses the banking crisis definition from the judging index of money market pressure, the fixed effect model regression from the work of Hagen and Ho (2007) under the assumption that this usage of the index is widely known and accepted, however, the availability of relevant variables as well as the ability of the author to access similar updated data may lead to data collection bias Moreover, some indicators only shows their significance with some lags, thus, more data seems needed to prevent the model from missing observation which could lead to sample size reduction problem Furthermore on lagged terms usage, there should be the trial and error processes for all possible lags of all relevant indicators, but it seems to be impractical when there are many variables; thus, the thesis has to compromise to use lags suggested from previous studies with some slight subjective changes Hence, there leaves a question on how one could test for suitable lags to be applied Unfortunately, all the institutional variables applied in the estimation model give insignificant results As mentioned, this pessimistic outcome maybe because of inappropriate dataset used, thus, the testing should be employed with more proper database according to newly empirical studies or at least the same database of Breuer et al (2006) from ICRG After all, like almost studies, there is no evidence found for the end of a banking crisis, only starts and 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development Economics,27(1), 263-283 Von Hagen, J., & HO, T K (2007).Money market pressure and the determinants of banking crises Journal of Money, Credit and Banking, 39(5), 1037-1066 APPENDICES Table 4.1 Banking crisis dates retrieved from MMP index Argentina 2001M3 2001M8 2008M12 2007M1 2001M11 2009M7 2008M1 2002M4 2010M3 2009M1 2004M5 2010M5 2009M11 2004M10 2010M1 Indonesia 2002M12 2005M4 2006M2 2006M4 2004M2 2006M10 2007M3 2007M8 2007M12 2008M9 2008M5 2008M4 2008M11 2008M8 2008M7 2009M2 2010M9 Japan 2001M11 Philippines Thailand 2002M9 2002M8 2001M11 2003M1 2003M7 2002M11 2003M11 2004M3 2003M3 2005M11 2004M6 2003M7 2007M7 2004M9 2004M1 2008M11 2009M5 2006M9 2010M8 Korea Singapore 2001M3 2003M4 2001M12 2001M4 2005M10 2002M3 2002M6 2008M11 2002M12 2002M9 2009M2 2006M12 2006M12 2009M4 2008M12 2007M5 2010M10 2009M6 2008M10 China 2005M1 2006M1 2008M12 Chile Paraguay 2008M8 2008M12 Brunei 2008M6 2001M7 2005M4 Brazil Costa Rica Malaysia 2002M6 Uruguay 2005M10 2010M6 2006M4 2006M3 2002M1 2008M1 2007M10 2003M8 2009M1 2008M7 2004M3 2010M4 2008M10 2007M6 2010M9 2009M5 2002M2 2009M11 2007M12 2008M2 Mexico 2003M4 Vietnam 2002M3 Colombia 2010M4 2004M12 2003M3 2003M5 2005M12 2004M3 2005M4 2006M4 2005M3 2005M12 2008M10 2006M12 2007M5 2010M12 2008M4 2007M6 2008M6 2007M8 2010M3 Table 4.4Linktest for specification error of logit model logit BC_MMP INF l12 BC_MMP MB_grDepreR_iCrdGDP_GR DEPOGDP_GR M2RES_GR VA PS GE RQ RL CC Logistic regression Prob> chi2 Number of obs = 1943 LR chi2(14) = 33.30 Pseudo R2 = 0.0389 = 0.0026 Log likelihood = -411.64834 -BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -INF | -.3266457 1266713 -2.58 0.010 -.5749169 -.0783745 | 1.237036 2891568 4.28 0.000 6702991 1.803773 MB_gr | 0254394 0167837 1.52 0.130 -.0074561 0583348 Depre| 0687579 0277797 2.48 0.013 0143107 123205 R_i| 0090064 0155471 0.58 0.562 -.0214654 0394781 CrdGDP_GR | 0226107 0113294 2.00 0.046 0004054 0448159 DEPOGDP_GR | -.0241729 0133022 -1.82 0.069 -.0502448 001899 M2RES_GR | -.0053557 0051703 -1.04 0.300 -.0154893 0047778 VA | -.0727918 1784815 -0.41 0.683 -.422609 2770254 PS | -.0436996 1796811 -0.24 0.808 -.3958681 308469 GE | -.3891117 4795028 -0.81 0.417 -1.32892 5506965 RQ | 009629 3931909 0.02 0.980 -.761011 780269 RL | 1470301 5874459 0.25 0.802 -1.004343 1.298403 CC | 2175405 369696 0.59 0.556 -.5070503 9421313 _cons | -2.842534 2100018 -13.54 0.000 -3.25413 -2.430938 BC_MMP(12) -linktest Logistic regression Prob> chi2 = Number of obs = 1943 LR chi2(2) = 34.43 Pseudo R2 = 0.0402 0.0000 Log likelihood = -411.08473 -BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -_hat | 1.951366 891996 2.19 0.029 2030855 3.699646 _hatsq | 1986192 1823874 1.09 0.276 -.1588537 556092 _cons | 1.050287 1.066478 0.98 0.325 -1.039971 3.140545 Table 4.5 Goodness of fit test of model lfit, group(10) table Logistic model for BC_MMP, GOODNESS-OF-FIT TEST (Table collapsed on quantiles of estimated probabilities) + + | Group | Prob | Obs_1 | Exp_1 | Obs_0 | Exp_0 | Total | | -+ + -+ -+ -+ -+ -| | | | 0.0324 | | 0.0372 | | | 5.0 | 6.8 | 190 | 190.0 | 186 | 187.2 | 195 | 194 | | | 0.0414 | | 7.7 | 189 | 186.3 | 194 | | | 0.0450 | 10 | 8.4 | 185 | 186.6 | 195 | | | 0.0484 | | 9.0 | 186 | 185.0 | 194 | | -+ + -+ -+ -+ -+ -| | | 0.0527 | | 9.8 | 188 | 184.2 | 194 | | | 0.0583 | 15 | 10.8 | 180 | 184.2 | 195 | | | 0.0670 | 11 | 12.1 | 183 | 181.9 | 194 | | | 0.0921 | 15 | 14.7 | 179 | 179.3 | 194 | | 10 | 0.3826 | 29 | 27.7 | 165 | 166.3 | 194 | + + number of observations = number of groups = 1943 10 Hosmer-Lemeshowchi2(8) = Prob> chi2 = 5.05 0.7524 Tabel 4.6 Full model multicollinearity test result Variable VIF SQRT VIF Tolerance R-Squared -INF 1.18 1.08 0.8500 0.1500 BC_MMP12 1.00 1.00 0.9964 0.0036 MB_gr 1.01 1.00 0.9922 0.0078 Depre 1.12 1.06 0.8911 0.1089 R_i 1.70 1.30 0.5897 0.4103 CrdGDP_GR 1.84 1.36 0.5444 0.4556 DEPOGDP_GR 1.67 1.29 0.5987 0.4013 M2RES_GR 1.55 1.25 0.6433 0.3567 VA 1.92 1.39 0.5201 0.4799 PS 3.11 1.76 0.3217 0.6783 GE 12.34 3.51 0.0811 0.9189 RL 21.13 4.60 0.0473 0.9527 CC 10.68 3.27 0.0936 0.9064 RQ 8.44 2.91 0.1185 0.8815 -Mean VIF 4.91 Table 4.7 Dropping significantly high correlated variables GE, RL: Variable VIF SQRT VIF Tolerance R-Squared -INF 1.17 1.08 0.8521 0.1479 BC_MMP12 1.00 1.00 0.9970 0.0030 MB_gr 1.01 1.00 0.9924 0.0076 Depre 1.12 1.06 0.8925 0.1075 R_i 1.60 1.26 0.6258 0.3742 CrdGDP_GR 1.81 1.34 0.5540 0.4460 DEPOGDP_GR 1.64 1.28 0.6090 0.3910 M2RES_GR 1.53 1.24 0.6556 0.3444 VA 1.53 1.24 0.6535 0.3465 PS 2.20 1.48 0.4548 0.5452 CC 8.26 2.87 0.1210 0.8790 RQ 5.93 2.44 0.1685 0.8315 -Mean VIF 2.40 Table 4.8 Dropping high correlated variables GE, RL and CC Variable VIF SQRT VIF Tolerance R-Squared -INF 1.17 1.08 0.8573 0.1427 BC_MMP12 1.00 1.00 0.9972 0.0028 MB_gr 1.01 1.00 0.9932 0.0068 Depre 1.12 1.06 0.8933 0.1067 R_i 1.59 1.26 0.6282 0.3718 CrdGDP_GR 1.75 1.32 0.5719 0.4281 DEPOGDP_GR 1.58 1.26 0.6319 0.3681 M2RES_GR 1.52 1.23 0.6588 0.3412 VA 1.31 1.14 0.7657 0.2343 PS 1.73 1.31 0.5785 0.4215 RQ 2.05 1.43 0.4883 0.5117 -Mean VIF 1.44 Table 4.9 Using interactive term of GE and RL Variable VIF SQRT VIF Tolerance R-Squared -INF 1.18 1.08 0.8506 0.1494 BC_MMP12 1.00 1.00 0.9970 0.0030 MB_gr 1.01 1.00 0.9924 0.0076 Depre 1.12 1.06 0.8919 0.1081 R_i 1.63 1.28 0.6139 0.3861 CrdGDP_GR 1.81 1.34 0.5537 0.4463 DEPOGDP_GR 1.64 1.28 0.6087 0.3913 M2RES_GR 1.53 1.24 0.6536 0.3464 VA 1.61 1.27 0.6211 0.3789 PS 2.23 1.49 0.4482 0.5518 GERL 2.51 1.58 0.3984 0.6016 CC 9.97 3.16 0.1003 0.8997 RQ 5.95 2.44 0.1680 0.8320 -Mean VIF 2.55 Table 4.10 Full model logit BC_MMP INF l12 BC_MMP MB_grDepreR_iCrdGDP_GR DEPOGDP_GR M2RES_GR VA PS GE RQ RL CC Logistic regression Prob> chi2 = Number of obs = 1943 LR chi2(14) = 33.30 Pseudo R2 = 0.0389 0.0026 Log likelihood = -411.64834 -BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -INF | BC_MMPL12 -.3266457 1266713 -2.58 0.010 -.5749169 -.0783745 | 1.237036 2891568 4.28 0.000 6702991 1.803773 MB_gr | 0254394 0167837 1.52 0.130 -.0074561 0583348 Depre | 0687579 0277797 2.48 0.013 0143107 123205 R_i | 0090064 0155471 0.58 0.562 -.0214654 0394781 CrdGDP_GR | 0226107 0113294 2.00 0.046 0004054 0448159 DEPOGDP_GR | -.0241729 0133022 -1.82 0.069 -.0502448 001899 M2RES_GR | -.0053557 0051703 -1.04 0.300 -.0154893 0047778 VA | -.0727918 1784815 -0.41 0.683 -.422609 2770254 PS | -.0436996 1796811 -0.24 0.808 -.3958681 308469 GE | -.3891117 4795028 -0.81 0.417 -1.32892 5506965 RQ | 009629 3931909 0.02 0.980 -.761011 780269 RL | 1470301 5874459 0.25 0.802 -1.004343 1.298403 CC | 2175405 369696 0.59 0.556 -.5070503 9421313 _cons | -2.842534 2100018 -13.54 0.000 -3.25413 -2.430938 Table 4.11 Restricted model without GE, RL, CC logit BC_MMP INF BC_MMP12 MB_grDepreR_iCrdGDP_GR DEPOGDP_GR M2RES_GR VA PS RQ Logistic regression Prob> chi2 = Number of obs = 1943 LR chi2(11) = 32.45 Pseudo R2 = 0.0379 0.0006 Log likelihood = -412.07303 -BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -INF | -.3219181 1268047 -2.54 0.011 -.5704507 -.0733856 BC_MMP12 | 1.246891 2886808 4.32 0.000 6810872 1.812695 MB_gr | 0257815 0167912 1.54 0.125 -.0071286 0586917 Depre | 0679522 027654 2.46 0.014 0137514 122153 R_i | 0100531 0149234 0.67 0.501 -.0191962 0393024 0447454 CrdGDP_GR | 0233479 0109173 2.14 0.032 0019503 DEPOGDP_GR | -.0228573 0128545 -1.78 0.075 -.0480517 002337 M2RES_GR | -.0054279 0051016 -1.06 0.287 -.0154269 0045711 VA | -.0018056 1476724 -0.01 0.990 -.2912381 2876269 PS | -.0071222 1360878 -0.05 0.958 -.2738494 2596051 RQ | -.0206309 2000792 -0.10 0.918 -.412779 3715171 _cons | -2.948038 1731015 -17.03 0.000 -3.287311 -2.608766 Table 4.12 Fixed effect model with lags xtlogit BC_MMP INF l12.BC_MMP MB_grDepre l36.R_i l12.CrdGDP_GR l6.DEPOGDP_GR M2RES_GR VA PS RQ, fe Conditional fixed-effects logistic regression Number of obs = 1511 Group variable: country1 Number of groups = 18 LR chi2(11) = 48.88 Obs per group: = avg = 83.9 max = 84 Log likelihood 83 =-279.62085Prob> chi2 = 0.0000 -BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -INF | -.4326759 1718959 -2.52 0.012 -.7695856 -.0957662 BC_MMP12 | 1.144859 3351207 3.42 0.001 4880346 1.801683 MB_gr | 0448394 0189506 2.37 0.018 007697 0819818 Depre | 0983399 0376113 2.61 0.009 0246231 1720567 R_i(36) | 0279047 0110093 2.53 0.011 0063268 0494825 CrdGDP_G(12) | 0370456 012508 2.96 0.003 0125303 061561 DEPOGDP_GR(L6)| -.0354308 0153012 -2.32 0.021 -.0654207 -.005441 0004024 0073081 0.06 0.956 -.0139212 0147261 M2RES_GR | VA | PS | RQ | 1.95909 1.370052 -.9584184 -.8472151 1.43 0.153 -.7261627 4.644342 8581543 -1.12 1.309447 -0.65 0.264 -2.64037 7235331 0.518 -3.413683 1.719253 Table 4.13 Random effect model with lags xtlogit BC_MMP INF l12.BC_MMP MB_grDepre l36.R_i l12.CrdGDP_GR l6.DEPOGDP_GR M2RES_GR VA PS RQ, re Random-effects logistic regression Number of obs = 1511 Group variable: country1 Number of groups = 18 Random effects u_i ~ Gaussian Obs per group: = 83 avg = 83.9 max = 84 Wald chi2(11) Log likelihood =-313.92006Prob> chi2 = = 48.62 0.0000 -BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -INF | -.413111 1467951 -2.81 0.005 -.7008241 -.125398 BC_MMP12| 1.159605 3339441 3.47 0.001 5050869 1.814124 MB_gr | 0441357 0189147 2.33 0.020 0070636 0812078 Depre | 0970619 0374037 2.59 0.009 0237521 1703717 R_i(36)| 0262877 0097476 2.70 0.007 0071827 0453927 CrdGDP_GR(12)| 0395713 0103906 3.81 0.000 0192061 0599365 DEPOGDP_GR(6)| -.0328716 014082 -2.33 0.020 -.0604719 -.0052713 M2RES_GR | 0033222 0067812 0.49 0.624 -.0099686 016613 VA | 1155592 1821686 0.63 0.526 -.2414846 472603 PS | -.0908372 1507151 -0.60 0.547 -.3862334 204559 RQ | -.0532946 2346824 -0.23 0.820 -.5132638 4066745 _cons | -3.156307 2033296 -15.52 0.000 -3.554826 -2.757788 -+ -/lnsig2u | -14.30817 43.4502 -99.46899 70.85265 -+ -sigma_u | 0007817 0169817 2.52e-22 2.43e+15 rho | 1.86e-07 8.07e-06 1.92e-44 Likelihood-ratio test of rho=0: chibar2(01) = 0.00 Prob>= chibar2 = 1.000 Table 4.14 Simple logit model with lags logit BC_MMP INF l12.BC_MMP MB_grDepre l36.R_i l12.CrdGDP_GR l6.DEPOGDP_GR M2RES_GR VA PS RQ Logistic regression Prob> chi2 = Number of obs = 1511 LR chi2(11) = 48.89 Pseudo R2 = 0.0722 0.0000 Log likelihood = -313.92003 -BC_MMP | Coef Std Err z P>|z| [95% Conf Interval] -+ -INF | -.413118 1468117 -2.81 0.005 -.7008636 -.1253723 BC_MMP12 | 1.1596 333979 3.47 0.001 505013 1.814187 MB_gr | 0441349 0189171 2.33 0.020 007058 0812118 Depre | 0970666 0374072 2.59 0.009 02375 1703833 R_i(36)| 0262878 0097487 2.70 0.007 0071807 0453949 CrdGDP_GR(12)| 0395718 0103918 3.81 0.000 0192042 0599394 DEPOGDP_GR(6)| -.0328699 0140837 -2.33 0.020 -.0604735 -.0052662 M2RES_GR | 0033208 0067821 0.49 0.624 -.0099718 0166134 VA | 1155499 1821923 0.63 0.526 -.2415405 4726403 PS | -.0908037 1507346 -0.60 0.547 -.3862381 2046307 RQ | -.0533487 2347126 -0.23 0.820 -.5133769 4066795 _cons | -3.1566 203355 -15.52 0.000 -3.555168 -2.758031 Table 4.15 Comparison of lagged terms of indicators in simple logit, FEM and REM Variables INF BC_MMP12 Simple logit model Fixed effect model -0.413118 0.005*** -0.4326759 1.1596 0.001*** Random effect model 0.012** -0.413111 0.005*** 1.144859 0.001*** 1.159605 0.001*** MB_gr 0.0441349 0.020** 0.0448394 0.018** 0.0441357 0.020** Depre 0.0970666 0.009*** 0.0983399 0.009*** 0.0970619 0.009*** R_i(36) 0.0262878 0.007*** 0.0279047 0.011** 0.0262877 0.007*** CrdGDP_GR(12) 0.0395718 0.000*** 0.0370456 0.003*** 0.0395713 0.000*** -0.0328699 0.020** -0.0354308 0.021** -.0328716 0.020** DEPOGDP_GR(6) M2RES_GR 0.0033208 0.624 0.0004024 0.956 0.0033222 0.624 VA 0.1155499 0.526 1.95909 0.153 0.1155592 0.526 PS -0.0908037 0.547 -0.9584184 0.264 -0.0908372 0.547 RQ -0.0533487 0.820 -0.8472151 0.518 -0.0532946 0.820 _constant Number of -3.1566 0.000*** -3.156307 0.000*** 1511 1511 1511 121 121 121 -313.92003 -279.62085 -313.92006 48.89 48.88 - - - 48.62 Prob> chi2 0.000 0.000 0.000 Pseudo R2 0.072 - - observations Number of crises Log likelihood LR chi2(11) Wald chi2(11) Note: - One, two, and three asterisks show significance levels of less than 10%, less than 5%, and less than 1% respectively ... countries - The dynamics of the spillover effect of together with this crisis to the rest countries of the world and the crisis relevant factors The their research showed that these two types of financial. .. reasoning, the index of money market pressure may capture the vulnerabilities of banking sector and be defined as ? ?the weighted average of changes in the ratio of reserves to bank deposits and changes... by the work of Falcetti and Tudela (2006) when they considered the similar lagged term into the model for determinants of banking crisis The hypothesis here is that last period of banking crisis