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The Academy of Economic Studies Bucharest
The Faculty of Finance, Insurance, Banking and Stock Exchange
DOFIN - Doctoral School of Finance and Banking
Monetary policy through the “credit-cost channel”.
A VECM APROACH FOR ROMANIA
MSc Student Dragomir Ioana
Supervisor Professor Moisă Altăr
Topics
Introduction
 Literature review
 The model
 Data description
 Methodology
 Estimation results
 Conclusions

Introduction


The aim of this paper is to contribute to the analysis of the
effects of interest-rate based monetary policy by means of
a model that blends the credit and cost channels of
monetary policy into a single, integrated "credit-cost
channel" (CCC).
The purposes of the model is to demonstrate that firms
reliance on bank loans (“credit channel”) could make
aggregate supply sensitive to bank interest rates (“cost
channel”), which are driven by the policy rate,
controlled by the central bank and by a credit risk
premium charged by banks on firms.
Literature review
Monetary policy impulses have persistent real effects in the economy:
 aggregate demand  credit channel
Getler and Gilchrist(1993);Bernake and Getler (1995);Trautwein (2000);
 aggregate supply  cost channel
Barth and Ramey (2001);Christiano and Eichenbaum (1997,2005);
Chowdhury(2006);
Ravenna and Walsh(2003,2006);
 aggregate demand and supply  credit-cost channel
Greenwald and Stiglitz (1988,1993);
Fiorentini and Tamborini (2002);
Passamani and Tamborini (2005,2006);
The model
-
The economy: 3 markets: Labor, Credit and Output
3 classes of agents:
firms, households and banks and a central bank;
The economy operates sequentially t, t+1, ..., production takes 1 period;
Firms
(t): plan production for sale at t+1;
(t): face uncertainty about revenue from output sales;
(t): hire workers in the labor market;
-
(t): borrow the wage bill in the credit market.

Households
(t): sell labor and receive their income- is saved for consumption in t+1;
(t): consumption is brought out of saving carried over from t-1;
Banks
offer deposits services to households at zero interest and standard debt
contracts to firms;
insure against credit risk by borrowing reserves from the central bank
at the policy rate;




-
The model
Firms
QN  0, QNN  0
Q(T ) jt 1  Q( N jt ),
Q( t ) jt 1
N jt
Q( t ) t 1
Pt 1
P
e
jt 1
P e jt 1
u jt
- output of firm j;
- labour force used by firm j;
- total output in the economy;
- market clearing price level;
 Pt 1u jt P
e
jt 1
 Pt 1  Pt 1  (u jt 1)
- price forecast for firm j;
- forecast error for firm j, i.i.d. random variable,
with unit expected value and E (u jt )  1
zero correlation across firms Cov(uit , u jt )  0
The model
Firms
The loans demanded by a firm at time t:
d
L
jt
 Wt N jt
against which the firm is committed to paying in t+1:
Ld jt Rt
d
,if solvency state is declared Pt 1  Q(t ) jt 1  L jt R
Pt 1  Q(t ) jt 1 ,if default state is declared Pt 1  Q(t ) jt 1  Ld jt Rt
Rt  (1  rt ) - the gross nominal interest rate charged by banks;
Wt
- the nominal wage
The firm expected one-period profit:
Z e jt 1  P e t 1  Q(t ) jt 1  Wt N tj Rt
The model
Firms
The first order condition for maximazing profit:
QN  wt R
e
jt 1
- curent real wage
wt  Wt / Pt
e
e
e
R jt 1  Rt /  jt 1  1  r t 1 - expected real interest rate
- expected inflation rate
e jt 1  Pe jt 1 / Pt  1   e t 1
The labour demand function:
N d jt  N d ( wt , rt ,  e jt 1 ),
N d w  0, N d r  0, N d   0
The output supply:
Q(t ) jt 1  Q( N d (wt , rt ,  e jt 1 ))
The model
Households
At period t, each household h choses the sequence:
Ch : C (t ) ht 1 , C (t  1) ht 2 ,....., N h : N ht , N ht 1
in order to maximize their utility: max C , N U ht  U (Ch , N h )
Constraints:
C (e t ) ht 1
P ht 1
Pt
Dt 1
Pt C (t  1) ht  Dt 1 , P e ht 1C (t ) ht 1  Dt ,
Dt  Dt 1  Pt C (t  1) ht  Wt N ht
- amount of consumption goods at t+1 for h
- price forecast for household h
- price of goods at t
- deposit due at t
The labour supply function
N s ht  N s ( wt ,  e ht 1 ),
N s w  0, N s   0
The model
Banks
The expected net profit of a bank:
s
L bt
BRt
Kt
Rt
t
Ls bt  Rt (1  t )  BR t K t  Ls bt  0,
- loans oferred equals Dbt - deposits collected by bank b;
- borrowed reserves from central bank;
- gross official interest rate;
- gross bank interest rate;
BR t  Ls btt
Kt  (1  kt )
Rt  (1  rt )
- credit risk premium;
- default probability: t
 1  F (u )  P(u jt  u )
(1  t  K t )
1  t
Rt 
,  t  log
 rt   t  kt
1  t
1  t
*
t
* 1)
t
u jt – is the critical value of the forecast of the firms regarding the clearing
market price noise and F is the cumulative function of ujt
1) *
The Model
Macroeconomic equilibrium
•Labour market
N ( wt , rt ,  t 1 )  N ( wt ,  t 1 )
d
s
•Output market
Lt  Wt  N t
rt  t  kt
•Credit market
Q( N ( wt , rt ,  t 1 ))  Dt / Pt 1
 t 1  Pt 1 / Pt  1
d
The Model
Shocks from credit variables
 t , kt
d
s

N

(
1

N
w
  (1  QN )) 
dwt
 1
d
s
s

dQ(t ) t 1    N w  QN  ( N w  N  ) 
d t 1    N d w  (1  N s w  (1  QN )) 


 (dkt  d t )
d
s
s
  (1  N w  (1  QN ))  ( N   N w )
If
QN  1 changes in k ,  have negative effects on
t
t
s
s
N  N w
wt , Q(t )t 1 ,  t 1
Data description
Monthly series covering the period 2000M01 - 2009M03:
Q t  s output - the industrial production index;
 t  s inflation rate - the consumer price index;
wt
kt
t
real wage rate - the total economy gross wage index / CPI;
monetary policy variable - the 3M interbank rate ROBOR;
credit risk premium - the average bank lending rate for the
private sector;
* the foreign variable - the interbank rate 3M EURIBOR;
kt
All variables, excluding interest rates are log–transformed.
All variables are seasonal-adjusted.
The base year of indices-2005.The gestation time of output s =12
The transmission mechanism
Response to an increase in the bank interest rate:
The Labour Market
The output-market
ND
Ns
Wt-1
_
A
Wt
AS
t
t+1
B
Nt
AD
Nt-1
A
B
Q(t)t+1
Q(t-1)t
Data description
.8
.7
.6
.5
average lending rate
EURIBOR_3M_SA
ROBOR_3M_SA
.4
.3
.2
.1
.0
2000
2001
2002
2003
2004
2005
2006
2007
2008
Data description
1.6
1.4
1.2
real gross wage index
consumer price index
industrial production index
1.0
0.8
0.6
0.4
2000
2001
2002
2003
2004
2005
2006
2007
2008
Data description
Results of the unit root tests
Test
I(1)
Augmented-Dickey
Phillips- Perron
Fuller
Series
Sig level
1% level 5% level 10% level
t-Statistics
Prob.
Ln_ipi_fore
Level
-2.59
0.5099
0.8242 0.6225
0.8494
-12.4653
0.0000 -12.3937
0.0000
-0.5541
0.4750 -0.7669
0.3819
-14.2194
0.0000 -14.2336
0.0000
-0.2475
0.5948 -3.5104
0.0006
-2.3390
0.0194 -2.8214
0.0051
-2.1972
0.4861 -1.3161
0.8787
-7.8283
0.0000 -8.2725
0.0000
-1.8818
0.6572 -1.4960
0.8253
-4.0080
0.0111 -3.9281
0.0140
-3.0139
0.1332 -3.0089
0.1346
-8.4819
0.0000 -8.7278
0.0000
-1.94
-1.61
First dif
Ln_w_r_g_i_cpi_sa
Level
-2.59
-1.94
-1.61
First dif
Ln_cpi_fore_sa
Level
-2.59
-1.94
-1.61
First dif
av_lend_rate_sa
Level
-4.04
-3.45
-3.15
First dif
Euribor_3m_sa
Level
-4.04
-3.45
-3.15
First dif
Robor_3m_sa
Level
First dif
-4.04
-3.45
-3.15
t-Statistics
Prob.
Methodology
VAR(p) ESTIMATION
wt
Q(t )t 1
LN_W_R_G_I_CPI_SA LN_IPI_FORE
LN_W_R_G_I_CPI_SA(-1)
LN_IPI_FORE(-1)
LN_CPI_FORE_SA(-1)
ROBOR_3M_SA(-1)
AV_LEND_RATE_SA(-1)
C
EURIBOR_3M_SA
R-squared
Adj. R-squared
Sum sq. resids
S.E. equation
F-statistic
Log likelihood
Akaike AIC
Schwarz SC
Mean dependent
S.D. dependent
 t 1
rt
kt
LN_CPI_FORE_SA ROBOR_3M_SA AV_LEND_RATE_SA
0.5934
0.2772
0.4214
0.0058
0.4386
-0.1226
0.5475
0.0586
0.8721
-0.0614
0.1112
-0.2700
0.0408
0.1751
0.0189
0.0024
0.9671
0.0166
-0.0202
0.0080
-0.0123
0.0348
-0.1147
-0.0702
0.8830
-0.0864
0.0203
0.8351
0.0320
-0.0339
-0.0245
0.1533
0.7312
0.0157
0.2767
0.9854
0.9846
0.0541
0.0229
1162.5320
262.8832
-4.6524
-4.4806
-0.0133
0.1847
0.9381
0.9344
0.0480
0.0216
259.9391
269.4645
-4.7721
-4.6002
0.0929
0.0843
0.9998
0.9998
0.0015
0.0038
75348.1000
460.7814
-8.2506
-8.0787
-0.0491
0.2442
0.9828
0.9818
0.0457
0.0211
979.8470
272.1552
-4.8210
-4.6492
0.2248
0.1560
0.9964
0.9962
0.0056
0.0073
4750.5470
388.0116
-6.9275
-6.7556
0.2252
0.1190
VAR ESTIMATION
Lag Length Selection
Stability condition check
Lag
0
1
2
3
4
5
6
7
8
LogL
LR
858.1876
NA
1622.0850 1423.9640
1645.8110 41.9246*
1665.3820
32.6817
1686.4620
33.1547
1709.6940
34.2834
1723.4540
18.9713
1743.2060
25.3126
1764.6360
25.3829
Root
0.9832
0.9618
0.79 - 0.09i
0.79 + 0.092i
0.5000
FPE
AIC
SC
HQ
0.0000 -16.4697 -16.2139 -16.3661
2.85E-20* -30.8171* -29.9218* -30.4545*
0.0000 -30.7925 -29.2577 -30.1708
0.0000 -30.6870 -28.5127 -29.8064
0.0000 -30.6109 -27.7971 -29.4712
0.0000 -30.5766 -27.1233 -29.1779
0.0000 -30.3583 -26.2656 -28.7006
0.0000 -30.2564 -25.5242 -28.3397
0.0000 -30.1871 -24.8153 -28.0114
Modulus
0.9832
0.9618
0.8034
0.8034
0.5058
p=1
the VAR satisfies the
stability condition test
Methodology
Structural cointegration method Johansen&Juselius
•Objective: The identification of the long-run structural
relationships
•Re specification nof
the model:
1
yt  0 zt   i xt 1   xt 1  0  1t  
i 1
x'   y 't , z 't , y 't  [ wt , kt , t , qt 12 ,  t 12 ], z 't  [k t ]
,  - matrices of coefficients;
xt 1- error correction mechanism;
     ',
*
 - columns: r cointegration vectors long-run relationships
 - elements: adjustment coefficients of variables towards their
long-run relationships
VECM ESTIMATION
Johansen Cointegration Test
Stability condition check
Hypothesized
Trace Test
Max-Eigen Test
No. of CE(s)
Statistic
5 % Critical Val 1% Critical Val Statistic
5 % Critical Val 1% Critical Val
None **
273.23
87.31
96.58
129.16
37.52
42.36
At most 1 **
144.06
62.99
70.05
76.84
31.46
36.65
At most 2 **
67.23
42.44
48.45
48.34
25.54
30.34
At most 3
18.89
25.32
30.45
16.64
18.96
23.65
At most 4
2.25
12.25
16.26
2.25
12.25
16.26
*(**) denotes rejection of the hypothesis at the 5%(1%) level
VECM: with 5 variables vector y’t = [wt, kt, qt+12 , t, t+12], 1
exogenous variable z’t =[k*t], 3 cointegrating relations and 0 lag.
Root
Modulus
1.00
1.00
0.95
0.69
0.33
1.00
1.00
0.95
0.69
0.33
the VEC satisfies the
stability condition test
Estimation results
The unrestricted model
Cointegration vectors2) CointEq1 CointEq2
CointEq3
wt
LN_W_R_G_I_CPI_SA(-1)
1
0
0
Q(t )t 1 LN_IPI_FORE(-1)
0
1
0
t 1 LN_CPI_FORE_SA(-1)
0
0
1
ROBOR_3M_SA(-1)
1.73***
2.82***
0.67***
t
AV_LEND_RATE_SA(-1) -2.52***
-2.09***
-1.11***
t
@TREND(00:01)
-0.01***
0 0.00***
C
0.54
-0.21
0.41
(* significant at 10%, **significant at 5%, *** significant at 1%)

k
r
The coefficients of the inter-bank rate in all of the 3
cointegration equations is positive and significant,
underlying the negative correlation between the
policy rate and the key variables of the economy.
The Beta coefficients are estimated based on the normalization of ’* S11*,
where S11 is defined in Johansen 1995
2)
The unrestricted model
wt
Q(t )t 1
 t 1
rt
kt
Error Correction: D(LN_W_R_G_I_CPI_SA) D(LN_IPI_FORE) D(LN_CPI_FORE_SA) D(ROBOR_3M_SA) D(AV_LEND_RATE_SA)
CointEq1
-0.556222
0.029921
0.013261
0.03645
0.082512
t-statics
[-7.15254]
[ 0.37560]
[ 0.98080]
[ 0.47492]
[ 3.18154]
CointEq2
0.221122
-0.016269
0.002465
-0.079155
0.004033
t-statics
[ 7.07453]
[-0.50812]
[ 0.45355]
[-2.56601]
[ 0.38688]
CointEq3
0.288707
-0.013893
-0.034869
-0.025133
0.038311
t-statics
[ 6.73715]
[-0.31649]
[-4.68013]
[-0.59427]
[ 2.68069]
C
-0.010355
0.00758
0.009788
-0.022671
-0.009163
t-statics
[-1.31760]
[ 0.94158]
[ 7.16365]
[-2.92306]
[-3.49618]
EURIBOR_3M_SA
0.504033
-0.188102
-0.037112
0.510108
0.170395
t-statics
[ 2.23312]
[-0.81354]
[-0.94572]
[ 2.28997]
[ 2.26370]
the short dynamics of : wt, t+1, Qt+1 are not explosive.
w adjusts significantly and rapidly in the direction of all three
long-term relations;
Q hardly adjusts to any long-term equilibrium relation;
 adjusts slowly and significantly in the direction of the 3th coEq.
The unrestricted model
The impulse response functions
Response to Cholesky One S.D. Innovations
Response of LN_IPI_FORE to ROBOR_3M_SA
Response of LN_W_R_G_I_CPI_SA to ROBOR_3M_SA
.010
.00000
.008
-.00025
.006
-.00050
.004
-.00075
.002
-.00100
.000
-.00125
-.00150
-.002
2
4
6
8
10
12
14
16
18
20
22
2
24
4
6
8
10
12
14
16
18
20
22
24
Response of ROBOR_3M_SA to ROBOR_3M_SA
Response of LN_CPI_FORE_SA to ROBOR_3M_SA
.0025
.024
.0020
.020
.0015
.016
.0010
.012
.0005
.008
.004
.0000
2
4
6
8
10
12
14
16
18
20
22
24
2
4
6
8
10
12
14
16
18
20
22
24
The unrestricted model
The impulse response functions
Response of AV_LEND_RATE_SA to Cholesky
One S.D. ROBOR_3M_SA Innovation
.010
.009
.008
.007
.006
.005
.004
2
4
6
8
10
12
14
16
18
20
The cointegration graph
1.0
22
24
0.8
Production, wages and
inflation
small
deviations from the long
term level.
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
2000
2001
2002
2003
2004
2005
2006
2007
2008
Estimation results
The unrestricted model
R-squared
Adj. R-squared
Sum sq. resids
S.E. equation
F-statistic
Log likelihood
Akaike AIC
Schwarz SC
Mean dependent
S.D. dependent
Lags
1
2
3
4
5
6
7
8
9
10
11
12
LM-Stat
32.44
20.85
28.65
31.56
28.10
18.31
18.51
13.98
22.39
39.13
21.18
33.36
Prob
0.15
0.70
0.28
0.17
0.30
0.83
0.82
0.96
0.61
0.04
0.68
0.12
0.9854
0.9846
0.0541
0.0229
1162.5320
262.8832
-4.6524
-4.4806
-0.0133
0.1847
0.9381
0.9344
0.0480
0.0216
259.9391
269.4645
-4.7721
-4.6002
0.0929
0.0843
0.9998
0.9998
0.0015
0.0038
75348.1000
460.7814
-8.2506
-8.0787
-0.0491
0.2442
The unrestricted model
Component
1
2
3
4
5
Joint
Jarque-Bera df
115.68
34.66
5.34
352.21
23.00
530.90
0.9828
0.9818
0.0457
0.0211
979.8470
272.1552
-4.8210
-4.6492
0.2248
0.1560
0.9964
0.9962
0.0056
0.0073
4750.5470
388.0116
-6.9275
-6.7556
0.2252
0.1190
Prob.
2
2
2
2
2
10
0.00
0.00
0.07
0.00
0.00
0.00
Estimation results
The restricted model
Cointegration Restrictions:
B(1,1)=1
B(2,2)=1
B(3,3)=1
B(3,2)=0
B(1,2)=0
B(3,1)=0
A(2,1)=0
A(3,1)=0
A(4,1)=0
A(3,2)=0
A(2,2)=0
A(5,2)=0
A(2,3)=0
A(4,3)=0
Chi-square(6)
1.879083
Probability
0.930476
Cointegrating Eq:
CointEq1
CointEq2
CointEq3
LN_W_R_G_I_CPI_SA(-1)
1.000
-0.847
LN_IPI_FORE(-1)
0.000
1.000
LN_CPI_FORE_SA(-1)
-1.319
0.336
ROBOR_3M_SA(-1)
1.918
2.529
AV_LEND_RATE_SA(-1)
-2.280
-1.413
@TREND(00:01)
0.000
0.003
C
0.021
-0.515
Error Correction:
CointEq1
CointEq2
CointEq3
D(LN_W_R_G_I_CPI_SA)
-0.362453
0.225052
D(LN_IPI_FORE)
0.000
0.000
D(LN_CPI_FORE_SA)
0.000
0.000
D(ROBOR_3M_SA)
0.000
-0.070136
D(AV_LEND_RATE_SA)
0.090732
0.000
0.000
0.000
1.000
0.043
-0.386
-0.005
0.395
-0.263843
0.000
-0.034894
0.000
0.15274
Conclusions
Empirical results show that, by way of the CCC
transmission mechanism the inter-bank rate is a codeterminant with negative sign of the long-run stochastic
equilibrium paths of the real wage rate, output and
inflation. The results for the premium risk variable reject
the same hypothesis, due to the lack of a better measure of
risk.