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. 1 . , 1 (print money)- 1 150 6-7 , 8.25 (4-8 ) , 150 . . 2008 1 2 650-800 2 .

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Bullard, J.B. (1999), Testing long-run monetary neutrality propositions: Lessons from the recent research, Federal Reserve Bank of St. Louis Review, 81 (6), 57.77. Gerald P.Dwyer JR and R.W. Hafer (1999), Are Money Growth and Inflation Still Related, Federal Reserve Bank of Atlanta, Economic Review, Second quarter of 1999. Leo Bonato (2007), Money and Inflation in the Islamic Republic of Iran, IMF, Working Paper, WP/07/119. Johansen, S. (1988), Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamics and Control, 12 (2/3), pp.231-254. Toshitaka Sekine (2001), Modeling and Forecasting Inflation in Japan, IMF, Working Paper, WP/01/82. L.Davaajargal (2005), Relationship between Money Growth and Inflation, Bank of Mongolia, Working Paper, Series # 11. D.Gan-Ochir (2008), Testing Long-Run Neutrality of Money in the Mongolia, (unpublished working paper; Bank of Mongolia).

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Anderson, O. Jr. (1952), The business test of the IFO-Institute for economic research, Munich, and its theoretical model, Revue delInstitut International de Statistique, No. 20, pp. 1-17.

Batchelor R. A., Orr A. B. (1988), Inflation expectations revisited, Economica, New Series, No. 55(219), pp. 317-331.

Berk J. M. (1999), Measuring inflation expectations: a survey data approach, Applied Economics, No. 31(11), pp. 1467-1480.

Carlson J. A. (1975), Are price expectations normally distributed?, Journal of American Statistical Association, No. 70(352), pp. 749-754.

Carlson J. A., Parkin M. (1975), Inflation expectations, Economica, No. 42, pp. 123-138.

Cunningham A. (1997), Quantifying survey data, Quarterly Bulletin: August 1997, Bank of England, pp. 292-300.

ECB (2002), Recent developments in consumers inflation perceptions, Monthly Bulletin, July, pp. 18-19, European Central Bank.

Forsells M., Kenny G. (2004), Survey expectations, rationality and the dynamics of euro area inflation, Journal of Business Cycle Measurement and Analysis, No. 1(1), pp. 13-42.

Kokoszczyski R., yziakT. (2009),The use of different measures of inflation expectations in monetary policy making. CCBSNBP survey results, paper presented during the CCBS ChiefEconomistsWorkshoponNewchallengesinassessing andmanaginginflationexpectations,19-20May2009,availableonrequest.

yziak T. (2003), Consumer inflation expectations in Poland, ECBWorking Paper, No. 287,EuropeanCentralBank.

yziakT. (2005), Inflation targeting and consumer inflation expectations in Poland. A success story?,JournalofBusinessCycleMeasurementandAnalysis,No.2(2),pp.185-212.

yziak T., Stanisawska E. (2006), Consumer inflation expectations. Survey questions and quantification methods the case of Poland,NBPPaper,No.26,NationalBankofPoland.

yziak T. (2010), Measuring consumer inflation expectations in Europe and examining their forward-lookingness, IFCBulletinNo. 33 (IFCs contribution to the 57th ISI Session,Durban,2009),IrvingFisherCommitteeionCentralBankStatistics,BankforInternationalSettlements,pp.155-201.

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. VAR , . VAR BVAR 2002 - . BVAR (MAPE) . - 800 Minnesota prior- - BVAR()=(2, 0.1, 1, 1) (MAPE) VAR(2)1 . 1- - . . , - . BVAR (OLS) SVAR . BVAR SVAR . , BVAR/SBVAR , , 2. VAR BVAR . .

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1 4VAR1-4VAR(2).

2 .- .

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(unanticipated) .

.- (2011) (SBVAR)Anderson, Paul A. 1979. Help for the Regional Economic Forecaster: Vector Autore-gression. Federal Reserve Bank of Minneapolis Quarterly Review.Crone, Theodore M. and McLaughlin, Michael P. 1999. A Bayesian VAR Forecasting Model for the Philadelphia Metropolitan Area. Federal Reserve Bank of Philadelphia Working Paper No.99-7. Doan, Thomas, A. 1990. RATS Users Manual. VAR Econometrics, Suite 612, 1800 Sherman Ave., Evanston, IL 60201.Eberts, Randall W. 1990. Can State Employment Declines Foretell National Business Cycles?. Federal Reserve Bank of Cleveland Economic Commentary. Gruben, William C. and Hayes, Donald W. 1991. Forecasting the Louisiana Econo-my. Federal Reserve Bank of Dallas Economic Review. Gruben, William C. and Long, William T. 1988. Forecasting the Texas Economy: Ap-plications and Evaluations of a Systematic Multivariate Time Series Model. Federal Reserve Bank of Dallas Economic Review. Hamilton, James D. 1994. Time Series Analysis. Princeton University Press, Princ-eton, New Jersey.Kinal, Terrence and Ratner, Jonathan. 1986. A VAR Forecasting Model of a Regional Economy: Its Construction and Comparative Accuracy. International Regional Sci-ence Review, vol. 10, no.2, p 113-126.Litterman, Robert B. 1980. A Bayesian Procedure for Forecasting with Vector Autore-gressions. Federal Reserve Bank of Minneapolis. Litterman, Robert B. 1984a. Forecasting and Policy Analysis with Bayesian Vector Autoregression Models. Federal Reserve Bank of Minneapolis Quarterly Review. Litterman, Robert B. 1984b. Specifying Vector Autoregressions for Macroeconomic Forecasting. Federal Reserve Bank of Minneapolis Staff Report 92. Otrok, Christopher and Whiteman, Charles. 1998. Bayesian Leading Indicators: Mea-suring and Predicting Economic Conditions in Iowa. International Economic Review, vol.39, no.4, p997-1014. Weller, Barry R. 1990. Predicting Small Region Sectoral Responses to Changes in Aggregate Economic Activity: A Time Series Approach. Journal of Forecasting, vol.9, p273-281. West, Carol T. 2003. The Status of Evaluating Accuracy of Regional Forecasts. The Review of Regional Studies, vol.33, no.1, p 85-103.

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The model is just identifiedConvergence achieved after 32 iterationsa = 1 0 0 0 0 0.2346 1 0 0 0 -0.1606 -0.0004 1 0 0 0.0181 -0.0266 0.0001 1 0 1.5819 -2.1734 11.8156 -23.7291 1b = 0.0914 0 0 0 0 0 0.0999 0 0 0 0 0 0.0337 0 0 0 0 0 0.0124 0 0 0 0 0 1.7411a_se = 0 0 0 0 0 0.0254 0 0 0 0 0.0088 0.0078 0 0 0 0.0035 0.0029 0.0086 0 0 0.4963 0.4146 1.2031 3.2589 0b_se = 0.0015 0 0 0 0

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0 0.0016 0 0 0 0 0 0.0006 0 0 0 0 0 0.0002 0 0 0 0 0 0.0286

4. SBVAR(3,1,1,1) FIML

The model is just identifiedConvergence achieved after 32 iterationsa = 1 0 0 0 0 0.2539 1 0 0 0 -0.1591 -0.0024 1 0 0 0.0175 -0.0266 -0.0002 1 0 1.3212 -1.9963 11.9378 -24.0774 1

b = 0.0934 0 0 0 0 0 0.1015 0 0 0 0 0 0.0338 0 0 0 0 0 0.0125 0 0 0 0 0 1.7593a_se = 0 0 0 0 0 0.0253 0 0 0 0 0.0086 0.0077 0 0 0 0.0035 0.0029 0.0086 0 0 0.4928 0.4122 1.2110 3.2702 0b_se = 0.0015 0 0 0 0 0 0.0017 0 0 0 0 0 0.0006 0 0 0 0 0 0.0002 0 0 0 0 0 0.0289

5. SVAR(3)

***** Vector Autoregressive Model ***** Dependent Variable = cpicore_sa_l R-squared = 0.9973 Rbar-squared = 0.9957 sige = 0.0002 Q-statistic = 0.4922 Nobs, Nvars = 44, 17 ******************************************************************Variable Coefficient t-statistic t-probability copper_l lag1 0.017477 0.860469 0.397107 copper_l lag2 0.011617 0.482420 0.633396

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copper_l lag3 -0.022995 -1.223626 0.231665 fis_sa_l lag1 0.012030 0.597308 0.555279 fis_sa_l lag2 0.040841 2.143074 0.041275 fis_sa_l lag3 -0.006595 -0.314883 0.755271 m2_sa_l lag1 -0.073641 -1.085596 0.287256 m2_sa_l lag2 0.145760 1.545887 0.133774 m2_sa_l lag3 -0.030444 -0.413237 0.682697 cpicore_sa_l lag1 0.744655 4.092415 0.000346 cpicore_sa_l lag2 -0.065790 -0.360649 0.721168 cpicore_sa_l lag3 0.132847 1.026622 0.313709 rate_ad lag1 0.001758 1.370770 0.181735 rate_ad lag2 -0.000359 -0.285447 0.777480 rate_ad lag3 0.000505 0.397165 0.694368 trend_l -0.036034 -1.555386 0.131499 constant 0.152495 0.477289 0.636998 ****** Granger Causality Tests *******Variable F-value Probability copper_l 1.074296 0.376524 fis_sa_l 1.773873 0.175905 m2_sa_l 1.070158 0.378211 cpicore_sa_l 50.737615 0.000000 rate_ad 0.658754 0.584562

Macroeconomic indicators MAPE percentage forecast errors based on 9 12-step-ahead forecasts Horizon copper_l fis_sa_l m2_sa_l cpicore_sa_l rate_ad 1-Quarter 2.03 4.23 0.44 0.33 36.37 2-Quarter 2.98 6.19 1.05 0.66 66.52 3-Quarter 4.14 8.60 1.54 1.10 94.25 4-Quarter 6.01 11.83 2.20 1.58 114.72 5-Quarter 9.50 14.21 3.07 2.09 121.23 6-Quarter 13.34 15.32 3.98 2.49 124.97 7-Quarter 15.67 16.84 4.92 2.57 143.15 8-Quarter 17.47 18.51 5.85 2.63 161.69

6. SBVAR(3, 0.1, 1, 0.5)

***** Bayesian Vector Autoregressive Model ***** ***** Minnesota type Prior ***** PRIOR hyperparameters tightness = 0.10 decay = 1.00 Symmetric weights based on 0.50

Dependent Variable = cpicore_sa_l R-squared = 0.9953 Rbar-squared = 0.9925 sige = 0.0003

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Nobs, Nvars = 44, 17 ******************************************************************Variable Coefficient t-statistic t-probability copper_l lag1 0.002738 0.622914 0.536631 copper_l lag2 0.000585 0.247749 0.805508 copper_l lag3 0.000021 0.013204 0.989526 fis_sa_l lag1 0.002934 0.587143 0.560178 fis_sa_l lag2 0.001331 0.506767 0.614907 fis_sa_l lag3 0.000341 0.192748 0.848064 m2_sa_l lag1 0.023982 1.798981 0.079043 m2_sa_l lag2 0.008214 0.978804 0.333152 m2_sa_l lag3 0.003149 0.545296 0.588368 cpicore_sa_l lag1 0.966463 19.184340 0.000000 cpicore_sa_l lag2 -0.025340 -0.636505 0.527821 cpicore_sa_l lag3 -0.001203 -0.043806 0.965262 rate_ad lag1 0.000268 0.858601 0.395322 rate_ad lag2 0.000023 0.138101 0.890805 rate_ad lag3 -0.000002 -0.016707 0.986747 trend_l -0.026734 -2.360931 0.022832 constant -0.164954 -1.520449 0.135718

Macroeconomic indicators MAPE percentage forecast errors based on 9 12-step-ahead forecasts Horizon copper_l fis_sa_l m2_sa_l cpicore_sa_l rate_ad 1-Quarter 1.78 2.54 0.31 0.46 19.08 2-Quarter 3.99 3.57 0.64 0.78 32.04 3-Quarter 4.86 4.46 0.95 1.15 43.43 4-Quarter 6.01 5.51 1.27 1.42 45.25 5-Quarter 7.18 7.78 1.50 1.65 54.34 6-Quarter 8.15 9.59 1.69 1.85 62.18 7-Quarter 8.74 9.53 1.74 2.06 67.03 8-Quarter 9.97 10.27 1.60 2.33 64.27

7. SBVAR(3, 0.1, 1, 1)

***** Bayesian Vector Autoregressive Model ***** ***** Minnesota type Prior ***** PRIOR hyperparameters tightness = 0.10 decay = 1.00 Symmetric weights based on 1.00 Dependent Variable = cpicore_sa_l R-squared = 0.9959 Rbar-squared = 0.9935 sige = 0.0002 Nobs, Nvars = 44, 17 ******************************************************************Variable Coefficient t-statistic t-probability

63

., .-, . : (BVAR)

copper_l lag1 0.004463 0.614196 0.542322 copper_l lag2 0.000500 0.117853 0.906733 copper_l lag3 -0.000737 -0.254137 0.800601 fis_sa_l lag1 0.004574 0.558075 0.579688 fis_sa_l lag2 0.003606 0.760761 0.450952 fis_sa_l lag3 0.000711 0.219108 0.827603 m2_sa_l lag1 0.028495 1.377657 0.175437 m2_sa_l lag2 0.014370 0.953027 0.345903 m2_sa_l lag3 0.004367 0.417250 0.678571 cpicore_sa_l lag1 0.927606 18.225003 0.000000 cpicore_sa_l lag2 -0.024508 -0.655469 0.515657 cpicore_sa_l lag3 -0.001346 -0.052070 0.958714 rate_ad lag1 0.000770 1.531739 0.132912 rate_ad lag2 0.000066 0.223576 0.824146 rate_ad lag3 0.000009 0.042651 0.966177 trend_l -0.034716 -2.819130 0.007252 constant -0.160761 -1.213623 0.231516 Macroeconomic indicators MAPE percentage forecast errors based on 9 12-step-ahead forecasts Horizon copper_l fis_sa_l m2_sa_l cpicore_sa_l rate_ad 1-Quarter 1.80 2.37 0.32 0.44 18.31 2-Quarter 4.04 3.23 0.66 0.77 31.89 3-Quarter 4.61 4.02 0.98 1.12 45.59 4-Quarter 5.31 5.39 1.34 1.35 47.48 5-Quarter 6.34 6.76 1.66 1.51 57.82 6-Quarter 7.09 8.50 1.90 1.69 66.04 7-Quarter 8.17 8.89 2.01 1.85 71.86 8-Quarter 9.80 9.69 2.00 1.99 69.98

8. SBVAR(3, 0.1, 1, 1)

***** Bayesian Vector Autoregressive Model ***** ***** Gibbs sampling estimates ***** ***** Minnesota type Prior ***** PRIOR hyperparameters tightness = 0.10 decay = 1.00 Symmetric weights based on 1.00 Equation 1 R-squared = 0.9431 Rbar-squared = 0.9094 sige = 0.0036 Nobs, Nvars = 44, 17 ndraws,nomit = 1250, 250 time in secs = 0.3440r-value = 50 *******************************************************************

64

-7

Equation 4 R-squared = 0.9953 Rbar-squared = 0.9925 sige = 0.0030 Nobs, Nvars = 44, 17 ndraws,nomit = 1250, 250 time in secs = 0.3020r-value = 50 *******************************************************************Variable Coefficient t-statistic t-probability variable 1 lag1 0.011296 0.025004 0.980165 variable 1 lag2 -0.005684 -0.023231 0.981571 variable 1 lag3 -0.001741 -0.010065 0.992015 variable 2 lag1 0.006087 0.011715 0.990706 variable 2 lag2 0.013186 0.043740 0.965309 variable 2 lag3 -0.004178 -0.019895 0.984217 variable 3 lag1 -0.002990 -0.002200 0.998255 variable 3 lag2 0.029565 0.031438 0.975062 variable 3 lag3 -0.000500 -0.000739 0.999414 variable 4 lag1 0.859621 0.260573 0.795638 variable 4 lag2 -0.014831 -0.006058 0.995194 variable 4 lag3 0.047586 0.028416 0.977459 variable 5 lag1 0.000224 0.007191 0.994295 variable 5 lag2 0.000367 0.019443 0.984575 variable 5 lag3 0.000180 0.013794 0.989056 dvariable 1 -0.020763 -0.025816 0.979521 constant 0.113057 0.013151 0.989567

9. SBVAR(3, 0.1, 2, 1)

***** Bayesian Vector Autoregressive Model ***** ***** Minnesota type Prior ***** PRIOR hyperparameters tightness = 0.10 decay = 2.00 Symmetric weights based on 1.00 Dependent Variable = cpicore_sa_l R-squared = 0.9957 Rbar-squared = 0.9932 sige = 0.0003 Nobs, Nvars = 44, 17 ******************************************************************Variable Coefficient t-statistic t-probability copper_l lag1 0.005314 0.733604 0.467172 copper_l lag2 0.000239 0.105354 0.916585 copper_l lag3 -0.000054 -0.053082 0.957913 fis_sa_l lag1 0.005703 0.687881 0.495222 fis_sa_l lag2 0.001020 0.405360 0.687223

65

., .-, . : (BVAR)

fis_sa_l lag3 0.000099 0.087929 0.930342 m2_sa_l lag1 0.037820 2.052121 0.046278 m2_sa_l lag2 0.004609 0.558024 0.579722 m2_sa_l lag3 0.000717 0.190807 0.849575 cpicore_sa_l lag1 0.923991 22.538499 0.000000 cpicore_sa_l lag2 -0.007460 -0.357392 0.722546 cpicore_sa_l lag3 -0.000429 -0.045037 0.964286 rate_ad lag1 0.000845 1.665856 0.103012 rate_ad lag2 0.000021 0.134726 0.893457 rate_ad lag3 0.000001 0.017895 0.985805 trend_l -0.033292 -2.754439 0.008583 constant -0.177176 -1.417275 0.163608

Macroeconomic indicators MAPE percentage forecast errors

based on 9 12-step-ahead forecasts Horizon copper_l fis_sa_l m2_sa_l cpicore_sa_l rate_ad 1-Quarter 1.78 2.49 0.32 0.45 19.04 2-Quarter 4.00 3.33 0.65 0.78 32.60 3-Quarter 4.56 3.86 0.99 1.12 45.73 4-Quarter 5.26 5.32 1.34 1.36 46.34 5-Quarter 6.26 6.59 1.64 1.53 55.65 6-Quarter 7.02 8.25 1.88 1.73 63.97 7-Quarter 8.11 8.86 1.97 1.86 69.99 8-Quarter 9.74 9.26 1.95 2.01 67.77

10. SBVAR(3, 0.1, 2, 1)

***** Bayesian Vector Autoregressive Model ***** ***** Gibbs sampling estimates ***** ***** Minnesota type Prior ***** PRIOR hyperparameters tightness = 0.10 decay = 2.00 Symmetric weights based on 1.00 Equation 4 R-squared = 0.9953 Rbar-squared = 0.9925 sige = 0.0026 Nobs, Nvars = 44, 17 ndraws,nomit = 1250, 250 time in secs = 0.3030r-value = 50 *******************************************************************Variable Coefficient t-statistic t-probability variable 1 lag1 -0.001079 -0.002357 0.998130 variable 1 lag2 0.003004 0.022151 0.982427

66

-7

variable 1 lag3 -0.000868 -0.014629 0.988395 variable 2 lag1 0.018572 0.036694 0.970895 variable 2 lag2 0.002797 0.017378 0.986213 variable 2 lag3 -0.001210 -0.017073 0.986455 variable 3 lag1 0.093035 0.078125 0.938083 variable 3 lag2 -0.006819 -0.013522 0.989273 variable 3 lag3 -0.002444 -0.010412 0.991740 variable 4 lag1 0.856954 0.341880 0.734069 variable 4 lag2 0.005388 0.004197 0.996671 variable 4 lag3 -0.013053 -0.021908 0.982621 variable 5 lag1 0.001807 0.054012 0.957170 variable 5 lag2 0.000360 0.037453 0.970293 variable 5 lag3 -0.000076 -0.017211 0.986346 dvariable 1 -0.067764 -0.088186 0.930129 constant -0.363556 -0.046907 0.962800

(MCI)- : /

?

: .- 1 [email protected]

2009 8

( ) (MCI)- . . (i) 2006 3 2008 2 , - . (ii) 2001Q1-2005Q4 , 2006Q1-2008Q2 , 2008Q3-2008Q4 . (iii) , 2009 2 .

, . , .

68

-7

I.

(MCI)1- . , .

, . , , , . , , , . .

, . ( /) . . , .

( )- MCI- . , . IS . .

MCI ( )- . 2.

1 ?, ? .

2 .

69

.- (MCI)- : / ?

, . .

. . ( )- .

, , MCI- .

MCI- , , .

. 2- , . 3- , , (MCI)- . 4- , , .

II. ,

(MCI) ( , )- .

, . , .

1990- MCI- , . MCI-

70

-7

. MCI .

. . 1 MCI- . MCI- . MCI .

MCI ( )- . ( , , ). MCI .

MCI- ( )- . . . MCI , .

.

, [1]

: - (- ); - ; - ; .

- . MCI- .

[2]

, . 0 t . [2] MCI- MCI (narrow MCI) .

1 , , - .

71

.- (MCI)- : / ?

MCI . , MCI- 2 .

I- MCI- . MCI- I (broad MCI) . Bernanke Gertler (1995) . . . (2007) SIMOM (2008)- . MCI- . I- .

, [1]

, - . MCI . [2]

, . , 0 t . MCI () () . [2]- MCI- t 0 - .

III. ,

3.1 ,

, . 2002 1 2009 2 . - - . , ,

72

-7

. 1 1- .

- Census X12-ARIMA - H-P . .

ADF 5%- ( 1- ). Newey-West - .

3.2 ,

MCI- . MCI- - . - SIMOM . MCI- . MCI- SVAR SIMOM . - . 2.

MCI- 2- [1] [1] . 1- . , 3- . .

1 :, 4

ave aveq t tlr lr= , ( )1

ave fc fc fc dct t t t tlr w lr w lr= + , -

, - , - , - , .

2 IS:IS:

, , , - , - .

73

.- (MCI)- : / ?

, . MCI- .

1- , (- )- , - , . , . , 1 - 2 1.0-1.5 , 1 2 - 1.12-1.22 10 2 - 2.9 .

1.

1- , , . , , , . , .

A) MCI- :

: 2002Q2-2009Q2 D-W=1.71SSE=0.031

B) MCI- :

: 2002Q2-2009Q2,

D-W=1.6SSE=0.03

: . Newey-West .

s.e (0.02) (0.30) (0.46) (0.75) (0.01) (0.05) (0.10)

s.e (0.017) (0.38) (0.40) (0.15) (0.63) (0.04) (0.14)

74

-7

.

, 2008 4 2009 2 . 2009 . ( )- - 1. 1- B- - 1- .

1. -

:

2007 2 2008 3 , , , - . 2009 2 - 1- , - , - , ( 1). 2009 1 2009 2 . 2008 , , ,

1 - .

75

.- (MCI)- : / ?

, .

3.3 (MCI),

3.2 MCI- .

[3]

[4]

- , - , - .

t 0 1 (- )- 1.3 5.3 . I- , . MCI ( (t=0)- 2007 1 )- 2- .

2. MCI

:

76

-7

2002- 2005 . .

2- 2 . , 2004 3 2005 2 MCI I . 2008 3 2 , , ( 2). 8 , 2- .

2.

MCI-

2002-2004 -2.0() -3.9 0.4 1.4

2000-2001

2004

2004-2006 2.6() 0.4 1.5 0.8

2006-2008 -6.2() -9.0 2.0 0.9

2007-2008 -

2008Q2-2009Q2

1.8() 10.0 -9.1 0.9

,

:

MCI- 2006 3 2008 2

77

.- (MCI)- : / ?

MCI 2007-2008 . 1 , , 100 , , , , .

2008 2 , , ( 2). , 2009 1 ( 3).

CI- 3- .

3. MCI

:

1 , , - . .

78

-7

2009 2 , , , ( 3).

, . ? . 4- .

4- 2001 2005 4 . , (I ) . 2006 1 2008 2 . , 2008 2 2008 4 .

4.

:

, 2009 2 , , .

79

.- (MCI)- : / ?

, , , . 1, - , , , , . , . 2, .

, . , - . (, ) . , ( , ) .

IV. ,

, . : 2008 ,

, , 2009 .

MCI- 2006 3 2008 2

1 .

2 .

80

-7

. 1, , 100 , , .

8

2002-2004 (), 2004-2006 (), 2006-2008 () 2008 2 2009 2 () . 2 . 2010 2 .

2009 2 , ( ) . , MCI- . . , MCI- .

2001 1 2005 4 . 2006 1 2008 2 , . 2008 2 2008 4 .

2009 2 , .

1 , , - . .

81

.- (MCI)- : / ?

. , , , , , .

MCI . MCI- SIMOM SVAR , 2, .

[1] Alfred V. Guender (2008), Monetary Condition Index, Princeton Encyclopedia of the World Economy, Department of Economics, University of Canterbury

[2] Bernanke, Ben.S. and Gertler, Mark (1995), Inside the Black Box: The credit Channel of Monetary Policy Transmission, Journal of Economic Perspectives, Volume 9, Number 4, Fall 1995.

[3] David G.Mayes-Matti Viren (1998), The Exchange Rate and Monetary Conditions in the Euro Area, Bank of Finland, Discussion Papers, 27/98

[4] G.R. Stevens (1998), Pitfalls in the Use of Monetary Conditions Indexes, Reserve Bank of Australian Bulletin

[5] N.R.Ericsson, E.S.Jansen, N.A. Kerbeshian and Ragner Nymoen (1997), Under-standing A Monetary Conditions Index, Norges Bank

[6] N.R.Ericsson, E.S.Jansen, N.A. Kerbeshian and Ragner Nymoen (2004), Inter-preting a Monetary Conditions index in Economic Policy, Norges Bank

[7] Paulo Soares Esteves (2003), Monetary Consitions Index for Portugal, Banco de Portugal, Economic Bulletin

[8] Wensheng Peng and Frank Leung (2005), A Monetary Conditions Index for main-land China, Hong Kong Monetary Authority Quarterly Bulletin, Feature Article

2 , .

82

-7

1. ADF

^ Level()

*

None

(b=a=0)

Inte

rcep

t(a0,b=0)

Tren

d &

intercept

(a0,b0)

ADF0H : 0=

y 0 + 0.000 I(0)dq 1 + 0.000 I(0)r 6 + 0.002 I(0)dl_r 0 + 0.015 I(0)dfex_r 2 + 0.000 I(0)

0 + 0.000 I(0)

0 + 0.000 I(0)

^ -t

k

sstt UXXbtrendaX ++++=

=

11

+

.*- 5%- ;

2.

( ne )- :

[X1] ( ) ( )USD RMBw wn USD MNT RMB MNTe e e=

USDw RMBw 1 :

[X2] 1USD RMBw w+ =

[X1]- , [X2]- :

[X3] ( ) ( )log( ) log( ) 1 logn USD USD MNT USD RMB MNTe w e w e= + , .

USD MNT . .

[X4] USD MNT USD RBM RMB MNTe e e=

83

.- (MCI)- : / ?

:

[X5] log( ) log( ) log( )USD MNT USD RMB RMB MNTe e e= +

[X5]- [X3]- :

[X6] ( )log( ) log( ) logn USD MNT USD USD MNTe e w e= + ( ) :

[X7]

P - , *P - - . :

[X8] ( ) ( )*USD RBMw wUSD RMBP P P= .

3.

X.3.1 MCI-

A)

DependentVariable:GAP

Method:LeastSquares

Date:07/28/09Time:13:07

Sample(adjusted):2002Q22009Q2

Includedobservations:29afteradjustmentsNewey-WestHACStandardErrors&Covariance(lagtruncation=3)GAP=C(1)+C(2)*(LR_AV_R_Q(-2))+C(3)*D(REER_L(-2))+C(8)*GAP(-1)+C(4)*D(CHI_G(-2))+0*D(M2P_L(-2))+C(6)*D(FEXP_R(-2))+C(7)

*D(P_COP_L(-2))

Coefficient Std.Error t-Statistic Prob.

C(1) 0.053178 0.020932 2.540517 0.0186C(2) -1.003425 0.304852 -3.291511 0.0033C(3) -0.809211 0.463498 -1.745877 0.0948C(8) 0.160090 0.096854 1.652897 0.1126C(4) 1.215923 0.758020 1.604077 0.1230C(6) 0.013771 0.010259 1.342412 0.1932C(7) 0.055361 0.024856 2.227257 0.0365

84

-7

R-squared 0.497504 Mean dependent var 0.011112AdjustedR-squared 0.360460 S.D.dependentvar 0.039483S.E.ofregression 0.031575 Akaikeinfocriterion -3.866412Sumsquaredresid 0.021933 Schwarzcriterion -3.536375Loglikelihood 63.06297 Hannan-Quinncriter. -3.763048F-statistic 3.630243 Durbin-Watsonstat 1.706363Prob(F-statistic) 0.011764

B) Jarque-Bera

C) LM

Breusch-GodfreySerialCorrelationLMTest:

F-statistic 1.067179 Prob. F(2,20) 0.3628Obs*R-squared 2.796395 Prob.Chi-Square(2) 0.2470

D) heteroskedastic Breusch-Pagan-Godfrey-

HeteroskedasticityTest:Breusch-Pagan-Godfrey

F-statistic 0.742941 Prob. F(12,16) 0.6949Obs*R-squared 10.37690 Prob.Chi-Square(12) 0.5829ScaledexplainedSS 4.617447 Prob.Chi-Square(12) 0.9696

85

.- (MCI)- : / ?

E)

X.3.2 MCI-

A)

DependentVariable:GAP

Method:LeastSquares

Date:07/22/09Time:11:50

Sample(adjusted):2002Q22009Q2

Includedobservations:29afteradjustmentsNewey-WestHACStandardErrors&Covariance(lagtruncation=3)GAP=C(1)+C(2)*(LR_AV_R_Q(-2))+C(3)*D(REER_L(-2))+C(8)*GAP(-5)+C(4)*D(CHI_G(-2))+C(5)*D(LP_L(-2))+0*D(FEXP_R(-3))+C(7)

*D(P_COP_L(-4))

86

-7

Coefficient Std.Error t-Statistic Prob.

C(1) 0.042809 0.016710 2.561797 0.0178C(2) -1.515267 0.378182 -4.006717 0.0006C(3) -1.115397 0.400194 -2.787138 0.0107C(8) 0.368661 0.139251 2.647455 0.0147C(4) 1.428435 0.629851 2.267893 0.0335C(5) 0.286985 0.154634 1.855894 0.0769C(7) 0.120639 0.041750 2.889541 0.0085

R-squared 0.518940 Mean dependent var 0.011112AdjustedR-squared 0.387741 S.D.dependentvar 0.039483S.E.ofregression 0.030894 Akaikeinfocriterion -3.910006Sumsquaredresid 0.020998 Schwarzcriterion -3.579969Loglikelihood 63.69509 Hannan-Quinncriter. -3.806643F-statistic 3.955383 Durbin-Watsonstat 1.608600Prob(F-statistic) 0.007817

B) Jarque-Bera

C) LM

Breusch-GodfreySerialCorrelationLMTest:

F-statistic 0.817266 Prob. F(2,20) 0.4559Obs*R-squared 2.191007 Prob.Chi-Square(2) 0.3344

87

.- (MCI)- : / ?

D) heteroskedastic Breusch-Pagan-Godfrey-

HeteroskedasticityTest:Breusch-Pagan-Godfrey

F-statistic 1.642284 Prob. F(12,16) 0.1751Obs*R-squared 16.00550 Prob.Chi-Square(12) 0.1910ScaledexplainedSS 7.380820 Prob.Chi-Square(12) 0.8315

E)

- .

2011 12

89

.

1.

/, , / .

, . , , . , , , , .

, , 2004-2011 , .

2- , 3- , 4- 2011 , 5- 2004-2011 , .

2.

.

GDP = C + I+ (X-M) (1)

, GDP- , - , I- , X-, M- .

(Y) - (Yf) (Yr ) .

Y = GDP +Yf + Yr (2)

- :

Y =C + I+ (X-M) +Yf + Yr (3)

( ) (CAB) .

91

.

3. ,

. - . 2- .

2. : -

- GNDI-C-I=S-I

SI=CAB

S-I=-(FDI+NFB-OINd)+NFA (1) (SI)+(FDI+NFBOINd)NFA=0(1)

- GNDIgCgIg=Sgg(2) SgIg=(NFBg+NDCg+NBOINg)(3) (2) (SgIg)+(NFBg+NDCg+NBOINg)=0

- GNDIpCpIp=SpIp(4)

SpIp=(FDIp+NFBp+NDCpM2NB

OINp)

(3) (SpIp)+FDI+NFBp+NDCpM2NB

OINp=0(5)

- GNDIbCbIb=SbIb=0 SbIb=0=(M2NFANDCOINb) (4) M2NFANDCOINb=0(6)

- X+MYfTrf=CAB

CAB=FDI+NFB+NEOR

NEO=OINf(7) (5) CAB(FDI+NFBOINfNFA)=0(8)

: GNDI , S-, I- , CAB- , C , -, - , Yf- , TRf- , NFB- , NDC- , NB- , FDI , 2- , NFA- , OIN- (, ), g- , p- , b- , f- , -

92

-7

, , . , . 3- .

3.

Y Yg Yp 0

-C -Cg -Cp 0

-I -Ig -Ip 0 , X -X 0

, -M M 0

Yf -Yf 0

Yr -Yr 0 (S-I) (Sg-Ig) (SP-IP) 0 CAB 0 -NFA -NFA NFA 0

FDI FDI -FDI 0

NFB NFBg NFBp -NFB 0

M -M2 M2-M 0

NDCg NDCp -NDC 0

NB -NB 0 -OINd -OINg -OINp -OINb -OINf 0 0 0 0 0 0 0

:

, , .

. /, , , . /, / ./ .

93

.

: - , .

: - .

/ / .

.

(i),(j) . (ij) ( ) i j (+,-) .

.

.

4. (2011 O)

, , 2011 . c .

4.1.

2011 10.2 83.1%- , 16.9%- . 13.5 53% , 47% . 80% , 20% 1 85%- c, 15%- / 4.1/

1 , .

94

-7

4.1. 2011 / /

/

(1)

c

(5)

(2) (3) (4) (6) -10215.9 1726.4 8489.5 0 7133.8 -1435.1 -5698.7 0 6332.3 - 923.7 -5408.6 0 , 6891.7 -6891.7 0 , -9528.1 9528.1 0 () -917.9 917.9 0 () 304.1 -304.1 0 -3250.0 -632.4 -2617.8 3250.2 0

283.2 2800.4 - 538.8 187.1 -2731.9 0 283.2 2800.4 -3083.6 0 4726.3 25.3 4701.0 -4726.3 0 1642.7 257.9 -1900.6 -1642.7 0 - 538.8 187.1 351.7 0 -351.7 - 538.8 187.1 351.7 0 571.3 641.1 -108.6 0 568.3 644.1 -1212.4 0 568.3 2376.4 2944.7 0 -1732.3 129.3 1603.0 0 3.0 -3.0 0 - 2.6 -2.6 0 0.4 -0.4 0 518.3 -222.1 -823.7 1564.1 -518.3 0 0 0 0 0 0 0

95

.

2011 . -3250.2

2636.4 , , 917.9 , 304.1 , .

-917.9 149.1 , , 768.7 . 1.0 .

4.2.

2011 3250.2 2.7 , - 30%- . - / 4.2/.

4.2. , , 2011

(Sp) (Ip) (Sp-Ip) (Sg) (I) (Sg-Ig) (CAB)

2809.7 5427.5 -2617.8 272.4 904.8 -632.4 -3250.2

(Sp-Ip)0 CAB

96

-7

- 2.6 - 24.2%- . 11.1 5.7 51.0% , 5.4 49.0% . - 52.6%, 49.9%, 25.8%- / 4.1/.

4.1. , ,

70.7%-, 28.8%- , . 45.2%- .

4.2.

86.6%- . , 2.6 , 1.9 , 66%-, 35%- . .

97

.

, , .

2011 632.4 - 5.8%- . 2.4 1.4 60.8% , 923.7 39.2% . - 24.4%- 3 . 24%- , .

4.3. , ,

45%- . 55% , 45% , . 42.9%-, 32.3%- .

4. 4.

98

-7

, , .

4.3.

- , .

2011 2671.1 283.2 , 2740.1 -351.7 .

4.5. ,

257.9 , 25.3 . .

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99

.

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-445.2 , .

, 128.1 , 130.9 . 1714.4 , , .

1212.4 47% , 53% .

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. 2011 358.3 , 35.0 . 295.4 , 2.6 .

66.1 , -4.7 -117.7 . 0.4 .

2011 2.4 (1799.9 ) .

100

-7

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2011 1.4 1603.0 , , 129.3 . 1.7 , 2.4 641.1 . 3.0 .

, - .

. , , .

2011 1212.4 . 571.3 (47%) , 641.1 (53%) .

101

.

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, 1 2011 277.6 -183.9 , 461.5 . 295.4 100.0 , 195.4 . 4.7 () .

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2011 1.6 , 129.3 . 2.4 . 4 . (37.0%), (73%) - . 67.6 (4%-) 1.8 (3.9 ) .

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102

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103

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104

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105

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106

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107

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108

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109

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110

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111

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114

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116

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Johanssen (1995), Phillips Hansen (1990) I(1) . Shin, Pesaran, Lee Garratt (1998) . Shin I(0) I(1) . . Shin Pesaran ARDL(autoregressive distributed lag) . .

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120

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-7

Acemoglu, D., Zilibotti, F.,. ( 1997). Was Prometheus unbending by chance? Risk, diversification, and growth. J. Polit. Econ. J. Polit. Econ., 105, 709775.Bencivenga, V., Smith, B. ( 1993). Some consequences of credit rationing in an endogenous growth model. J. Econ. Dynam.Control , 17, 97122.Boyd, J. & Prescott, E. (1986). Financial intermediary-coalitions. J. Econ. Theory , 211232.Goldsmith, R. W. (1969). Financial Structure and Development. . Yale University Press.Greenwood, J., Jovanovic, B.,. ( 1990). Financial development, growth and the distribution of income.. 98,. J. Polit. Econ, 10761107.Gurley, J.G., Shaw, E.S. (1955). Financial aspects of economic development. Am. Econ. Rev., 45, 515538.Husam-Aldin N. Al-Malkawi. (2012). Financial Development and Economic Growth in the UAE. International Journal of Economics and Finance.Jordan Shan & Qi Jianhong. (2006). Does Financial Development Lead Economic Growth? The Case of China. ANNALS OF ECONOMICS AND FINANCE, 197-216.LEVINE, R. (1993, 1997). Financial Development and Economic Growth: Views and Agenda. Journal of Economic Literature, 688726.Levine, R. (1997). Financial development and economic growth: views and agenda. J. Econ. Lit. 35, 688726.M. Shabri Abd. Majid. (1997). Does Financial Development and Inflation Spur Economic. ChulalongkorMn . JSohuarbnrail A obfd E. Mcoanjoidm :i cDs, 161-184.Ndako, U. B. (2007). STOCK MARKETS, BANKS AND ECONOMIC GROWTH: A TIME SERIES EVIDENCE FROM SOUTH AFRICA.Pagano, M. (1993). Financial markets and growth: An overview. Eur. Econ. Rev. 37, , 613622.Shittu, Ayodele Ibrahim. (2012). Financial Intermediation and Economic Growth in Nigeria. British Journal of Arts and Social Sciences, Vol.4 No.2... (2004). . , .

.-1 [email protected]

2009 9

, . , . (i) , , , . , . (ii) 6 , , , , 2 , . (iii) , . (iv) , . (v) , , .

()- . , . . ( - ), . ( - ) . (- ) .

126

-7

I.

. , . , 2 , , . . , , . , .

C , , , , . 2006-2007 , . 3 . ( ) ( ) . , . , . . , . , , . , , .

Han-nan Berger (1989, 1991)- , Maudos Guevara (2004) . 3 , 75- 6 2003 1 2009 2

127

.-

. - . . , .

. 2- , . 3- , panel . , 5- , 6- , .

II. ,

, ex-post 1 . , 2002-2009 1- . 7 2 2009 2 18.9 . , 2008 2 . 2008 4 , . III .

. , , . , . . :

- , - , - , . , .

1 . (2006), . . .

128

-7

1. ,

: ,

2006 . , .

, , 3 , ()- . 3 2009 2 - . 7 18 . IV .

2007 . , , . , .

129

.-

. , III .

III.

3.1

Hannan Berger (1989, 1991)- . , , . Raquel Vecente (2005) . .

[1]

t i , , u i , i . t i j , t , k , t m , . . :

( )

, . , . , . . .

130

-7

. , . .

( , ). . .

. . . .

, ( )

. . , , . . , . . Herfindahl M .

(CRMD). M . .

Herfindahl- (HERF_D). Herfindahl- . .

( )

. ,

131

.-

. , . , .

. . . , . .

. . . , . . . . . .

M2 . 2008 M2 , , . . 2 . .

. () . - . - . - . , .

( - ). , . . .

132

-7

(D) ()- , , . . .

3.2

[1]- 6 7 . 1- . (cross section SUR ), (fixed effect)- panel EGLS Eviews 6.1 .

(cross section)- (6 )- (fixed effect)- . (two ways fixed effect) . (peri-od fixed) . . (cross section fixed) (redundant fixed effect) 2- X.2.2- . . . , , , .

(Wooldridge (2002)). cross section .

[1]- 1-, 2- .2.1- . . .

133

.-

1.

C -0.23 0.00***

( ) , IIP 0.02 0.06*

SIZE_S_D -0.19 0.00*** SIZE_D 0.02 0.00*** , CIR -0.02 0.01**

, ( ) 4 CR4D -0.12 0.00***

( ) OUT_GAP -0.18 0.01** D(INF) 0.09 0.00***2 2 M2_G_Q(-2) -0.03 0.04**

1 IBR (-1) 0.05 0.04** . D(ER_L) 0.05 0.01**

, - SM -0.05 0.08*

(D)

DEPOSIT_RATE(-1) 0.78 0.00***

0.89

156

Crosssectionfixedeffecttest 2.59 0.03

***, **, * 1%, 5%, 10%- . :

, . , , (IIP) 1 0.02 . 2005-2008 8 0.16 . (SIZE_D)

134

-7

. . , . , (SIZE_S_D) , . . 1 0.2 . 2002-2009 17 0.35 .

, . , , (CIR) 1 0.02 . 2003-2008 CIR 11 0.22 . 2009 2 15 , .

, , - . 1 . , . . , 4 (CR4D) 2002-2008 9 1.0 . .

(output gap)- . . . , 1 0.18 . ,

1 (CR(m)) m .

135

.-

. , , . . , (D(INF)) 1 0.1 . . 2 (M2_G_Q) 1 2 0.03 . () . - . - (IBR) . - 1 0.05 . 2004 2008 , - - .

. . . . . . . 1 0.05 . . , 2008 4 2009 1 .

. 3 . . . , - (SM) 1 0.05 . .

, . 1 . 0.8 1%- . 80 .

136

-7

[1]- 2- . 6 .

2.

:

, , - 2002-2005 , 2007-2009 . 2008 , , , , 2 , .

137

.-

IV. ,

4.1 ,

, Ho Saunders (1981)- Dealership , Maudos Guevara (2004) . , , , . , . .

[2]

t i , (spread), e i , i . . :

t i j ;

t i , k ;

t m ;

t n ;

, l .

, , , . , . :

( )

. , (OC)- .

138

-7

, . . .

. . . , .

. . McShane Sharpe (1985) , (RISKAVER)- . , .

. . . / . (SIZE)- .

. , . , (MG)- . . . , .

139

.-

. . . , , . , . .

. Saunders Schumacher (2000) , . , . . (IIP1), (IIP2), (IIP3)- . , , . . Demirguc-Kunt Huizinga (2000) (LIQ)- , ( + ) . , . .

, ( )

, . . . .

, . . , 2 ( )

140

-7

. , - .

. 2 . :

m1 CR(m);

Herfin-dahl - (HERF) .

( )

. . . , . . - GARCH (SD)- .

. . . , . . , (NPL)- .

. . GARCH (ERRISK)- . , . , .

1 .

141

.-

( )

, . , , - (GDP), - (GROWTH), - (GDP_GAP)- . - - - HP .

. (INF) , , . Ben Naceur (2003), Casu et al (2004) Diaz & Olivero (2005) .

. - . . , . (FD)- - , - .

() . . . . - . , (RESER)- .

(IBR). - . - .

(D)

. , , . Drakos (2003), Casu (2004) .

142

-7

. . . , . , , . .

4.2 ,

[2]- 6 7 . 3.2 . (cross section fixed) (redundant fixed effect) 2-, 3- X.3.1.2 X.3.2.2- . . panel .

[2]- 2 2-, 3- . 10 . .

2 . ,

A B

C -0.07 0.30 0.56 0.00***

( )(+), RESER 1.06 0.00*** 1.31 0.00***

, AOC

0.97 0.00*** 1.22 0.00***

,

RISKAVER 0.40 0.00*** 0.24 0.01***

,

IIP 0.11 0.00*** 0.10 0.00***

( +),

LIQ -0.09 0.07* -0.11 0.01***

SIZE_S 0.40 0.00***

SIZE_L - - -0.09 0.00***

143

.-

,

CIR 0.09 0.00*** 0.12 0.00***

, ( )Herfindahl- HERF_A 0.97 0.05** - -3 CR3A - - 0.50 0.00***

( )

, NPL 0.31 0.00*** 0.52 0.00***

- IRRISK1 0.01 0.00*** - -

ERRISK1 - - 0.0005 0.00***

( )- GDP_G 0.15 0.12 - -

INF 0.07 0.05** - -

6 D(INF(-2)) - - 0.09 0.01***

2 M2_G_Q(-1) - - -0.11 0.00***

IBR 0.18 0.01*** 0.24 0.00***

, - FD - - -0.04 0.10*

, - SM(-3) - - -0.46 0.00***

(D) T -0.01 0.00*** - -

1- S1 -0.02 0.00*** -0.03 0.00***

0.94 0.97

(N) 156 156

Crosssectionfixedeffecttest(F) 61.1 0.00 61.3 0.00

***, **, * 1%, 5%, 10%- . :

()- . - , . (RESER) , . , 1 1.1-1.3 . 2007 - RESER 2007-2009

144

-7

2 ( 1). - , . 3- - - , . . , (AOC) 1 1.0-1.2 . 2002-2008 AOC 1.1 . 2009 AOC 0.1 .

(RISKAVER) , . , 1 0.25-0.4 . RISKAVER 2002-2005 2 9%- 2005-2008 . 2009 1 2 0.5 . (IIP) , . , 2004-2008 8 0.8 . .

(LIQ) . 2005-2008 14 1.4 . (SIZE_S) . . 6 .

(SIZE_L) . , , . 2005-2008 6 74 6.7 . 2008 2 , , . .

145

.-

, (CIR) 1 0.1 . 2003-2008 11 1.1 .

, . Herfindahl- (HERF_A) 0.1 1 . 3 1 0.5 . .

(NPL)- . NPL 1 0.3-0.5 . 2009 . . - . . (INF)- 1 0.7 . , , . . 2 , . , 2008 17 1.2 . . . 2 , . 2 1 1 0.11 . 2002-2008 2 .

()- . - , . - . , , - - . . - 1 0.18-0.24 . 2002-2007 -

146

-7

2008 .

, , (FD)- . , . , - 10 0.4 . 2002-2007 53 2.1 . . 1 . (networth) . . , , - 1 0.46 . , 1 . 1 . 1 1 .

[2]- B 6 , , 3- .

1 .,.- (2008), :, ,.

147

.-

3. ,

:

( ), , , , , , , , . 2008 2 ( ), , - , , , , 2 . 2008 3 , . , , , .

148

-7

V. ,

5.1

95 . . , , , , . . 2008 2 ( ), , - , , , , , 2 . , . . - . , .

, , . , , , /, , , 1%- . , . 1 1 . , , . , . , , , . 2007-2008 - , 5%-

149

.-

. 2009 2 - .

, . , . , . . , . , - , - . , , . , 1 0.3 .

. - . - . - . - , - . . , . 2008 2009 . , .

2009 , . , . . , , 1 .

1 , .

150

-7

, . 4 - . . , . , .

, , . , . , , , .

, 2 , . , . - , . . , . , , . , , , .

5.2

, , . , . (interest rate smoothing)- , .

151

.-

- , . . - , . , .

, , . . , . , . . , , . , .

1998 , . . , . . , , . .

, . , . 2 , 5 . , , . , .

152

-7

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[12] Mahamudu Bawumia (PhD), Franklin Belnye & Martin Enoch Ofori (2005 Bank of Ghana), The Determination of Bank Interest Spreads in Ghana: An Empirical Analysis of Panel Data

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[14] Philip Brock & Helmut Franken (2003), Measuring the Determinants of Average and Marginal Bank Interest Rate Spreads in Chile, 1994-2001

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[16] Santiago Carb Valverde & Francisco Rodrguez Fernndez (2005), Margins, Specialization and Agregate Risk Behaviour in European Banking

[17] Tarsila Segalla Afanasieff, Priscilla Maria Villa Lhacer & Marcio I. Nakane ( 2002, Central bank of Brazil), The Determinants of Bank Interest Spread in Brazil

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153

.-

1.

2002 2003 2004 2005 2006 2007 2008 2009*

SPREAD,% , 28.0% 17.0% 15.0% 17.0% 15.0% 11.0% 10.0% 10.0%

DEPOSITRATE,% 18.0% 20.0% 20.0% 16.0% 17.0% 15.0% 13.0% 14.0%

RESER,% (+), 12.0% 10.0% 8.0% 8.0% 8.0% 7.0% 6.0% 6.0%

AOC,% , 2.0% 1.8% 1.3% 1.2% 1.2% 1.0% 0.9% 1.0%

RISKAVER,% , 7.0% 8.0% 8.0% 9.0% 9.0% 9.0% 9.0% 10.0%

IIP,% , 8.0% 8.0% 6.0% 10.0% 13.0% 13.0% 14.0% 21.0%

NPL,% , 1.0% 1.0% 3.0% 3.0% 3.0% 2.0% 2.0% 6.0%

LIQ, % ( +), 36.0% 20.0% 29.0% 19.0% 13.0% 11.0% 5.0% 12.0%

CIR, % , 96.0% 100.0% 97.0% 95.0% 87.0% 88.0% 89.0% 104.0%

SIZE_S,% 10.0% 11.0% 13.0% 13.0% 14.0% 18.0% 22.0% 23

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