DOCTORAL SCHOOL OF FINANCE AND BANKING
The bank lending channel in
Romania
-Solving the Supply versus Demand Puzzle-
Student: Stoica Mihai
Supervisor: Professor Moisă Altăr
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)
Domestic credit provided by banks (% GDP)
2000
2000
1999
Bulgaria
1999
Poland
Bulgaria
1998
Poland
1997
Romania
1998
Romania
Czech Republic
Czech Republic1997
Hungary
Hungary
1996
1996
1995
1995
0
20
40
60
80
0
20
40
60
80
100
120
140
• the Romanian banking system is overwhelmingly oriented
towards short term credit
• an important substitution effect has occurred
Description of variables
Series Label
Series descripti on
cragr
Real ROL denominated credit for firms
craltag
Real non-governmental cred it other than for firms
ip
Industrial p roduction index (monthly volu me index)
ipsa
Industrial p roduction index (monthly volu me 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)
Unit root tests
Variable
ADF test
Test
t-statistic
specification
(level)
F-statistic
(a=0,
 =1)
PP test
t-statistic
t-statistic
t-statistic
t-statistic
(1st
(modified
(level)
(1st difference)
difference)
specification)
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% level
critical values for the Dickey Fuller T est 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)
TSLS estimat ion
OLS estimation
Variable
Elasticity estimates (t-statistic
Variable
values in parentheses)
values in parentheses)
r_nbr
0.7429
r_nbr
0.2439
M0r(-1)
0.04179
er
0.4911
ar(1)
Adj.R 2  0.509 DW  2.07
0.4941
(5.257)
(3.5257)
R 2  0.52
0.0281
( 0.3931)
( 0.3931)
ar(1)
0.2366
(3.437)
(3.437)
er
0.824
( 3.090)
( 4.223)
M0r(-1)
Elasticity estimates (t-statistic
Instruments: cragr(-1) r_nbr(-1) m0r(-1) er
R 2  0.523
Adj.R 2  0.508 DW  2.08
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
Response to Generalized One S.D. Innovations ± 2 S.E.
Response of D(CRAGR) to D(M0R)
Response of D(M0R) to D(M0R)
.05
Response of R_NBR to D(M0R)
.06
Response of IPSA to D(M0R)
.015
.05
.05
.04
.04
.010
.04
.03
.03
.03
.02
.02
.01
.01
.005
.02
.01
.000
.00
.00
.00
-.01
-.01
-.005
-.01
-.02
-.02
-.03
1
2
3
4
5
6
7
8
9
10
-.010
1
2
3
4
5
Null hypothesis
M 0r does not Granger cause cragr
M 0r does not Granger cause r_nbr
M 0r does not Granger cause ipsa
6
7
8
9
10
-.02
1
2
3
4
5
2
6
7
8
9
10
1
(Wald) statistic
 2 ( 3) -7.543
 2 ( 3) -7.2595
 2 ( 3) -1.0201
2
3
4
5
6
7
8
9
p-value
0.0565
0.0641
0.7964
10
•response of credit to an interest rate innovation
Response of D(CRAGR) to Generalized One
S.D. R_NBR Innovation
.04
.03
.02
.01
.00
-.01
-.02
1
2
3
4
5
6
7
8
9
10
Null hypothesis
 2 (Wald) statistic
p-value
r_nbr does not Granger cause cragr
 2 ( 3) -16.482
0.0009
cragr does not Granger cause r_nbr
 2 ( 3) -5.287
0.1519
•Response of industrial production to a bank loan innovation
Response of IPSA to Generalized One
S.D. D(CRAGR) Innovation
Null hypothesis
 2 (Wald) statistic
p-value
ipsa does not Granger cause cragr
 2 (3) -1.109
0.7748
cragr does not Granger cause ipsa
 2 (3) -2.418
0.4902
.03
.02
.01
.00
-.01
-.02
-.03
1
2
3
4
5
6
7
8
9
10
A simple regime switching model-disequilibrium
model (Maddala Nelson (1974))
Dt  X 1't 1  u1t
St  X 2' t  2  u2t
Qt  min( Dt , St )
f Qt (qt )  f Q / Dt St (qt )  f Qt / St  Dt (qt )
f Qt / Dt St (qt ) 


qt
g Dt St (qt , z)dz
f Qt / Dt St (qt ) 

g
qt
Dt St ( z, qt )dz
Considering the simplifying assumption 12  0 we will get
the following likelihood function:
L( ) 
T
 log(G )
t
w here
Gt  f1 (qt )  F2 (qt )  f 2 (qt )  F1 (qt )
t 1
f1 (qt ) 
 1

'
2
exp 
(qt  X 1t 1 ) 
2
2  1
 2 1

f 2 (qt ) 
 1

'
2
exp 
(
q

X

)
t
2t 2 
2
2  2
 2 2

1
1
F1 (qt ) 
F2 (qt ) 
1
2  1
1
2  2
 1

'
2
exp 
( z  X 1t 1 ) dz
2
qt
 2 1



 1

'
2
exp 
( z  X 2t  2 ) dz
2
qt
 2 2



Initial conditions
1.
Qt 
'
X it i

2.
3.
dt 
qt
(d )
qt
(s)
'
X 1t

 ut
 1 and  2
with i=1,2


st 
1

'
X 2t
2

X 1(td ) '  1  u1 for


d t  st

~
~
(s)
X 2t '  2  u 2


~
~
for

d t  st
Results of the Monte Carlo
experiment on starting values (10,000
1  ( 10 , 11, 12 )'
simulations)
 2  (  20 ,  21)'
X 2t  ( x20t , x21t )'
X 1t  ( x10t x11t x12t )'
X ijt ~ N (0,1)
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
X tD  (r _ nbr, ipsa)'


X 2S  (r _ nbr, deprsa, M 0r (1))'



ML estimates of Model 1
Variable
r_nbr
ipsa
Demand equation (z-
Supply equation (z-
statistic in parentheses)
statisitc in parentheses)
-1.341
1.239
( 2.985)
0.010
(3.764)
-
(3.5049)
deprsa
-
0.109
( 0.7106)
m0r
-
0.343
( 2.9357)
i
0.0286
0.0436
R 2  0.59
Adj.R 2  0.56
L  195 .84
Model 2-
final specification
X 1D  (r _ nbr, ipsa, er (1), deptr)'




X 2S  (r _ nbr, deprsa, M 0r (1), craltag)'




ML estimates of model 2
Variable
r_nbr
Ipsa
Demand equation (z-
Supply equation (z-
statistic in parentheses)
statisitc in parentheses)
0.3585
0.6731
( 0.487)
0.0099
( 2.470)
-
( 2.606)
Actual fitted graph Model 2
er(-1)
0.7148
-
.1
(1.8517)
Deptr
0.13487
-
Deprsa
-
0.2538
( 1.638)
.0
-.1
( 2.527)
m0r(-1)
-
0.270
( 3.054)
0.5974
Craltag
-.2
( 8.113)
-.3
-.4
95
i
0.0358
0.0273
R 2  0.72
Adj.R 2  0.69
L  212 .031
96
97
98
actual
99
00
fitted
01
02
Probabilities of Demand/Supply regime
 t  pr( Dt  St )  pr( X 1't 1  u1t  X 2' t  2  u2t )
 pr(u1t  u 2t  X 2' t  2  X 1't 1 )
t 
( X 2' t  2  X 1' t  2 ) / 
1

2

e
u 2 / 2
du
where    12   22  2 12
Real exchange rate
7.4
Industrial production
5.0
7.3
4.9
7.2
4.8
7.1
4.7
7.0
4.6
6.9
4.5
6.8
4.4
4.3
6.7
95
96
97
98
ER
99
00
HPTREND
01
02
95
96
97
98
IPI
99
00
HPTREND
01
02
Model 3-considering interest rate endogeneity
ML estimates of model 3
Variable

r_nbr
ipsa
Demand equation (z-
Supply equation (z-
statistic in parentheses)
statisitc in parentheses)
0.1085
0.7378
( 0.1319)
0.0085
( 2.2294)
-
( 2.309)
er(-1)
0.7307
-
(1.779)
deptr
0.1287
-
deprsa
-
0.2606
( 1.949)
( 2.3026)
m0r(-1)
-
0.2616
( 2.811)
0.6215
craltag
( 8.113)
i
0.0347
0.0297
R 2  0.70
Adj.R 2  0.68
L  208 .668
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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*** 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