The bank lending channel in Romania

-Solving the Supply versus Demand Puzzle-

Student: Stoica MihaiSupervisor: Professor Moisă Altăr

DOCTORAL SCHOOL OF FINANCE AND BANKING

Theoretical background

According to the bank lending channel transmission mechanism, banks respond to a monetary contraction by reducing the supply of bank loans.

Two conditions must hold simultaneously for the bank lending framework to be valid:

•the central bank must be able simply by conducting monetary policy measures to influence the supply of bank loans- i.e.banks are not able to frictionlessly substitute the out-flowing deposits

•some firms must be dependent on bank loans- i.e. firms are not able to frictionlessly substitute between bank loans and another types of loans due to information problems

Identification of the bank lending channel

Bernanke and Blinder (1992)- they observe the reaction of the aggregate bank lending to a change in monetary policy stance

Kashyap and Stein (1994), Favero, Giavazzi, Flabbi (1999), de Bondt (2000), Kakes and Sturm (2000) - improve the identification of the lending channel by using desegregated bank balance sheet data.

Hallsten(1999) and Italiano(2001) use interest rate spreads (e.g. the spread between banking sector lending rate and the overnight interest rate).

The hypothesis of this paper:

The Romanian bank loan is supply determined (a bank lending channel is at work)

The econometric evidence (sample 1995:01 2003:01):

• a preliminary regression and VAR analysis

• estimating a set of disequilibrium models

The main finding:

The Romanian loan market is supply driven, being characterised by a state of disequilibrium throughout the sample period.

Descriptive analysis of the Romanian loan market

• slow structural reforms, weak confidence in the national currency and in the domestic banking lead to a process of acute process of demonetisation and disintermedition

Broad money (%GDP)

0 20 40 60 80

1995

1996

1997

1998

1999

2000

Bulgaria

Poland

Romania

Czech Republic

Hungary

Domestic credit provided by banks (%GDP)

0 20 40 60 80 100 120 140

1995

1996

1997

1998

1999

2000

Bulgaria

Poland

Romania

Czech Republic

Hungary

• the Romanian banking system is overwhelmingly oriented towards short term credit

0%

20%

40%

60%

80%

100%

Composition of Nonguvernamental Credit (term structure)

medium and long term credit

short term credit

• an important substitution effect has occurred

ROL and Foreign Currency Denominated Credit

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

ROL

foreigncurrency

Series Label Series description

cragr Real ROL denominated credit for firms

craltag Real non-governmental cred it other than for firms

ip Industrial p roduction index (monthly volume index)

ipsa Industrial p roduction index (monthly volume index)-seasonally adjusted

depr Total real ROL denominated deposits

deprsa Total real ROL denominated deposits-seasonally adjusted

er Real exchange rate (ROL/USD)

M0r Real monetary base

r_nbr Real lending rate to nonbanking sector

(note: all variables are in logs except for the lending rate)

Description of variables

Unit root tests

ADF test PP testVariable

Test

specification

t-statistic

(level)

F-statistic

(a=0, =1)

t-statistic

(1st

difference)

t-statistic

(modified

specification)

t-statistic

(level)

t-statistic

(1st difference)

cragr C -1.635 1.900 -5.284** -1.134 -1.443 -5.311**

Craltag C -2.285 3.730 -7.253** 0.807 -2.238 -7.245**

r_nbr C -4.640** - - - -5.029** -

ipsa C -4.617** - - - -12.015** -

deprsa C -1.108 0.705 -9.341** 0.208 -1.284 -9.398**

er C -1.250 1.206 -7.889** -0.671 -1.285 -7.778**

M0r C -2.524 3.181 -7.153** 0.204 -2.258 -7.186**

(critical values for the ADF and PP tests with intercept are: 3.499 -1% level, 2.891-5% level, 2.582 10% level and without intercept–2.589-1% level, -1.944-5% level, -1.614-10% levelcritical values for the Dickey Fuller Test based on the OLS F Statistic for a sample size of 100 are: 6.70-1%level, 5.57-2.5% level,4.71-5% level ,, 3.86-10%level)

Preliminary regression and VAR analysis

A. Regression analysis (cragr as dependent variable)

O L S e s t i m a t i o n

V a r i a b l e E l a s t i c i t y e s t i m a t e s ( t - s t a t i s t i c

v a l u e s i n p a r e n t h e s e s )

r _ n b r)223.4(

7429.0

M 0 r ( - 1 ))437.3(

2439.0

e r)3931.0(

04179.0

a r ( 1 ))5257.3(

4911.0

52.02 R 2.07DW 509.0. 2 RAdj

T S L S e s t i m a t i o n

V a r i a b l e E l a s t i c i t y e s t i m a t e s ( t - s t a t i s t i c

v a l u e s i n p a r e n t h e s e s )

r _ n b r)090.3(

824.0

M 0 r ( - 1 ))437.3(

2366.0

e r)3931.0(

0281.0

a r ( 1 ))257.5(

4941.0

I n s t r u m e n t s : c r a g r ( - 1 ) r _ n b r ( - 1 ) m 0 r ( - 1 ) e r

523.02 R 2.08DW 508.0. 2 RAdj

B. VAR analysis( variables: cragr, M0r, r_nbr, ipsa lag order: 3 sample:1995:01 2003:01)

B.1 Impulse response functions and Granger causality tests

•responses to a monetary innovation

-.02

-.01

.00

.01

.02

.03

.04

.05

1 2 3 4 5 6 7 8 9 10

Response of D(CRAGR) to D(M0R)

-.03

-.02

-.01

.00

.01

.02

.03

.04

.05

.06

1 2 3 4 5 6 7 8 9 10

Response of D(M0R) to D(M0R)

-.010

-.005

.000

.005

.010

.015

1 2 3 4 5 6 7 8 9 10

Response of R_NBR to D(M0R)

-.02

-.01

.00

.01

.02

.03

.04

.05

1 2 3 4 5 6 7 8 9 10

Response of IPSA to D(M0R)

Response to Generalized One S.D. Innovations ± 2 S.E.

Null hypothesis 2 (Wald) statistic p-value

M0r does not Granger cause cragr )3(2 -7.543 0.0565

M0r does not Granger cause r_nbr )3(2 -7.2595 0.0641

M0r does not Granger cause ipsa )3(2 -1.0201 0.7964

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of D(CRAGR) to Generalized OneS.D. R_NBR Innovation

•response of credit to an interest rate innovation

Null hypothesis 2 (Wald) statistic p-value

r_nbr does not Granger cause cragr )3(2 -16.482 0.0009

cragr does not Granger cause r_nbr )3(2 -5.287 0.1519

•Response of industrial production to a bank loan innovation

-.03

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of IPSA to Generalized OneS.D. D(CRAGR) Innovation

Null hypothesis 2 (Wald) statistic p-value

ipsa does not Granger cause cragr )3(2 -1.109 0.7748

cragr does not Granger cause ipsa )3(2 -2.418 0.4902

A simple regime switching model-disequilibrium model (Maddala Nelson (1974))

ttt uXD 11'1

ttt uXS 22'2

),min( ttt SDQ

)()()( // tDSQtSDQtQ qfqfqftttttt

t

ttttt qtSDtSDQ dzzqgqf ),()(/

t

ttttt qtSDtSDQ dzqzgqf ),()(/

Considering the simplifying assumption we will get the following likelihood function:

012

)()()()( here w)log()( 12211

ttttt

T

tt qFqfqFqfGGL

2

1'12

111 )(

2

1exp

2

1)(

ttt Xqqf

2

2'22

222 )(

2

1exp

2

1)(

ttt Xqqf

dzXzqFtq

tt

2

1'12

111 )(

2

1exp

2

1)(

dzXzqFtq

tt

2

2'22

222 )(

2

1exp

2

1)(

Initial conditions

tiitt uXQ '1. with i=1,2 21 and

2.

1

'1 tt Xd

2

'2 tt Xs

3. for ~

11

~)(

1)( ' uXq d

td

t tt sd

~

22

~)(

2)( ' uXq s

ts

t for tt sd

Results of the Monte Carlo experiment on

starting values (10,000 simulations)

)',,( 1211101 )',( 21202

)'( 1211101 tttt xxxX )',( 21202 ttt xxX )1,0(~ NX ijt

Two step OLS

Parameter True value Mean Std. Dev.

10 7 6.9417 0.1095

11 5 4.9579 0.111

12 2 1.9829 0.0878

20 7 6.9756 0.0804

21 3 2.9880 0.0920

1 0.5 0.5041 0.0590

2 0.5 0.5054 0.0596

Results on Monte Carlo experiment on ML estimates (10,000 simulations)

Maximum Likelihood

Parameter True value Mean Std. Dev.

10 7 7.002 0.1065

11 5 5.002 0.1029

12 2 2.0003 0.0809

20 7 7.001 0.0811

21 3 2.998 0.0662

1 0.5 0.4815 0.0521

2 0.5 0.4870 0.0531

Model 1- a parsimonious specification

)',_(

ipsanbrrX Dt )')1(0,,_(2

rMdeprsanbrrX S

M L e s t i m a t e s o f M o d e l 1

V a r i a b l e D e m a n d e q u a t i o n ( z -

s t a t is t i c i n p a r e n t h e s e s )

S u p p l y e q u a t i o n ( z -

s t a t is i t c i n p a r e n t h e s e s )

r _ n b r)985.2(

3411

.-)764.3(

239.1

i p s a)5049.3(

010.0 -

d e p r s a -)7106.0(

109.0

m 0 r -)9357.2(

343.0

i 0286.0 0436.0

59.02 R 56.0. 2 RAdj 84.195L

Model 2- final specification

)',)1(,,_(1

deptreripsanbrrX D )',)1(0,,_(2

craltagrMdeprsanbrrX S

M L e s t i m a t e s o f m o d e l 2

V a r i a b l e D e m a n d e q u a t i o n ( z -

s t a t i s t i c i n p a r e n t h e s e s )

S u p p l y e q u a t i o n ( z -

s t a t i s i t c i n p a r e n t h e s e s )

r _ n b r)487.0(

3585.0

)470.2(

6731.0

I p s a)606.2(

0099.0 -

e r ( - 1 ))8517.1(

7148.0 -

D e p t r)638.1(

13487.0

-

D e p r s a -)527.2(

2538.0

m 0 r ( - 1 ) -)054.3(

270.0

C r a l t a g)113.8(

5974.0

i 0358.0 0273.0

72.02 R 69.0. 2 RAdj 031.212L

-.4

-.3

-.2

-.1

.0

.1

95 96 97 98 99 00 01 02

actual fitted

Actual fitted graph Model 2

Probabilities of Demand/Supply regime

)(

)()(

1'12

'221

22'211

'1

tttt

ttttttt

XXuupr

uXuXprSDpr

/)(2/2

'12

'2 2

2

1tt XXu

t due where 1222

21 2

6.7

6.8

6.9

7.0

7.1

7.2

7.3

7.4

95 96 97 98 99 00 01 02

ER HPTREND

Real exchange rate

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5.0

95 96 97 98 99 00 01 02

IPI HPTREND

Industrial production

Model 3-considering interest rate endogeneity

M L e s t i m a t e s o f m o d e l 3

V a r i a b l e D e m a n d e q u a t i o n ( z -

s t a t i s t i c i n p a r e n t h e s e s )

S u p p l y e q u a t i o n ( z -

s t a t i s i t c i n p a r e n t h e s e s )

r_nbr )1319.0(

1085.0

)2294.2(

7378.0

i p s a)309.2(

0085.0 -

e r ( - 1 ))779.1(

7307.0 -

d e p t r)949.1(

1287.0

-

d e p r s a -)3026.2(

2606.0

m 0 r ( - 1 ) -)811.2(

2616.0

c r a l t a g)113.8(

6215.0

i 0347.0 0297.0

70.02 R 68.0. 2 RAdj 668.208L

Conclusions

•Romanian bank lending was mainly supply driven throughout our sample

•however, the bank lending channel of monetary policy is not complete due to the bank loan neutrality over output

•the sporadic demand regime periods (spanning from 1997 until 1999 ) were due to a demand decline in the context of harsh economic conditions

•from the year 2001 onwards the process of remonetisation was quite vigorous, re-establishing loan market equilibrium

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Oliner, S.D.,. Rudebush G.D (1995), “Is there a Bank lending Channel for Monetary Policy?”, FRB San Francisco Economic Review, No.2, pp.3-20.

Ramey V. (1993), “How Important is the Credit Channel in the Transmission of Monetary Policy?”, Carnegie-Rochester Conference series on Pubic Policy, 39, December, pp. 1-45.

Romer C., Romer D. (1993), “Credit Channel or Credit Action? An Interpretation of Postwar Transmission Mechanism”, NBER Working Paper No. 4485.

Sealey C.W. (1979), “Credit Rationing in the Commercial Loan Market: Estimates of a Structural Model under Conditions of Disequilibrium”, The Journal of Finance,Vol.34,No .3,June, pp. 689-702.

Stiglitz, J. E., and A. Weiss (1981), Credit Rationing in Markets with Imperfect Information, American Economic Review, 71, 393-410.

Walsh, C. E., “Monetary Theory and Policy”, MIT Press 1998, Ch. 7

Watson M. W. (1994), “Vector Autoregression and Cointegration”, in Hand-book of econometrics

*** National Bank of Romania : Annual Reports, Monthly Bulletins

*** IMF Country Reports 03/123 May 2003, 03/12 Jan. 2003, 03/11 Jan. 2003, 02/254 Nov. 2002