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The Academy of Economic Studies, Bucharest
DOCTORAL SCHOOL OF FINANCE AND BANKING
International Transmission of
Monetary Policy Shocks
The Case of Romanian Economy
MSc Student: ELENA BOJEŞTEANU
Supervisor: Professor MOISĂ ALTĂR
Bucharest, 2006
Contents
 Motivation
 The analysis of comovements using PCA
 Identifying monetary policy shocks using a reaction
function for the ECB
 The transmission of shocks – a SVAR approach
 Concluding remarks and further research
Key References
 Two-country models:
- Mundell Fleming (1962);
- Svensson and van Wijnbergen (1989);
- Obstfeld and Rogoff (1995);
- Chari, Kehoe and McGrattan (1997, 2000);
- Engel and Devereux (2003).
 Empirical Investigations:
- Cushman and Zha (1997);
- Christiano, Eichenbaum and Evans (1996, 1998);
- Betts and Devereux (1999);
- Bordo and Murshid (2002);
- Pesaran et al. (2005).
Romanian Economy
 The last five years: improved performance in terms of economic
expansion, strengthening disinflation, reduction in budget deficit
and unemployment.
 A small open economy: a degree of financial and commercial
openness exceeding 70% in the last 8 years. Main trading
partner: European Union (over 70% of the total for exports, and
more than 60% of the total imports. Trading currency: more than
60% settlement of exports and imports in euro, and approx.
30%in US dollar.
 Capital account liberalization schedule. Romania is increasingly
integrating into world markets and more precisely, into
European structures.
 Question: how do the commercial and financial linkages affect
the Romanian main economic indicators? Is there a comovement
in the economic variables, a synchronization of business cycles,
or Romanian is still in an incipient phase of integration?
Tracing comovements using Principal Component Analysis
 The method generates a new set of variables, called principal
components
 PC are orthogonal to each other
 Each principal component (pc) is a linear combination of the
original variables
 The coefficients of each of the linear combinations are called
loadings (or weights)
 The first principal component explains the greatest amount of
the total original variance
 The sum of the pc variances equals the total variance of the initial
system
Tracing comovements using Principal Component Analysis
GDP growth rates
Variance Explained (%)
The Principal Components for GDP growth rates
in Germany, France, Italy, Hungary, The Netherlands, Austria, UK and Romania
Ge
sample 1998:1-2005:4
Fr
1
100%
It
Hu
0.9
90%
Nt
0.8
80%
At
UK
0.7
70%
0.6
60%
0.5
50%
0.4
40%
0.3
30%
0.2
20%
0.1
10%
0


1
2
3
4
5
Principal Component
6
7
0%
Factor weights
RO
-0.36
-0.42
-0.41
-0.38
-0.38
-0.34
-0.35
-0.19
0.01
0.16
0.34
-0.29
0.14
-0.10
0.53
-0.22
0.15
-0.06
-0.11
-0.65
0.40
0.04
0.85
0.23
Percent
Comp. explained
Percent
explained
(cumm.)
1
46.95
46.95
2
3
14.83
10.47
61.78
72.25
The first component loading for Romania is small, which shows a lower correlation with the
rest of the system. Sayek and Selover (2002) state that the first principal component might be
thought of as the business cycle followed by the Western nations.
For the second component, Romania has a much higher loading than the rest of the
countries. The low and positive Romania’s loading on the first component may be a sign that
the macroeconomic evolution in this country was in general out of step with the rest of the
group.
Tracing comovements using Principal Component Analysis
Real interest rates
Factor weights
Variance Explained (%)
The Principal Components for real interest rates
in Germany, France, Italy, Hungary, The Netherlands, Austria, UK and Romania
Ge
sample 1998:1-2005:4
Fr
1
100%
It
Hu
0.9
90%
Nt
0.8
80%
At
UK
0.7
70%

0.04
0.11
0.09
-0.36
0.22
0.17
0.22
0.17
0.27
0.10
0.33
-0.51
-0.59
0.34
-0.22
-0.85
-0.24
0.6
60%
0.5
50%
0.4
40%
0.3
30%
0.2
20%
1
72.55
72.55
0.1
10%
2
3
10.61
7.53
83.16
90.69
0

RO
-0.39
-0.40
-0.40
-0.33
-0.35
-0.34
-0.35
1
2
3
Principal Component
4
0%
Percent
Comp. explained
Percent
explained
(cumm.)
A higher degree of comovement in the financial sector that in the real one. The first principal
component explains a substantial portion of the behaviour of interest rates across countries
and can be interpreted as the common element of real interest rates.
An atypical pattern for Romania is not out of the question, considering the high correlation
with the second component.
Possible Interpretations for Monetary Policy Shocks
Three general strategies for isolating monetary policy shocks:

The recursiveness assumption – based on the estimation of a reaction
function for the monetary authorities
- Christiano (1996);
- Christiano, Eichenbaum and Evans (CEE, 1996, 1997);
- Clarida, Gali and Gertler (1997);
- Cushman and Zha (1997).

The narative approach
- Romer and Romer (1989)

Long-run neutrality of money
- Pagan and Robertson (1995)
Identifying the monetary policy shocks using a reaction
function for the ECB
 An exogenous monetary policy shock, εt - formalized by CEE
(1998) as being the disturbance term in an equation of the form:
St = f(—t) + εt ,
where St is the instrument used by the monetary authority and
f(—t) is a linear function that captures the policy makers’
responses to variations in different economic variables, as they
are known at time t.
 An augmented reaction function for the Euro area:
it = (1-ρ)·α+(1-ρ)·β·E[πt+n] + (1-ρ) ·γ·yt + ρ· it + εt
.
Identifying the monetary policy shocks using a reaction
function for the ECB
Studies estimating the reaction
function using data before EMU:
Data:
 ex-post available data
 survey data
Gerdesmeier and Roffia (2003);
Gerlach(2003);
Surico (2003);
Carstensen and Colavecchio (2005).
Methodology:
 GMM for ex-post available data (a popular technique
in the rational-expectation context (Clarida, 1998)).
Problem: selection of instruments.
 OLS for survey data. Problem: constructing the data.
Identifying the monetary policy shocks using a reaction
function for the ECB
Ex-post available data
1996:01-2005:04 from ECB and Eurostat databases

Interest rates: the interbank ON interest rate, the 3M EURIBOR, and the
10Y government bond yield

Price indices: annualized HICP and alternatively the core inflation
(HICP - All items, excluding energy, food, alcohol and tobacco)

Output gap: from three measures for potential GDP: a Hodrick-Prescott
filter (the smoothing parameter equal to 1600 for quarterly data), a
linear and a quadratic trend. The three methods yield fairly similar
results.

Monetary aggregates: M3; a money gap was also used (the deviation of
money growth from the reference value of a constant growth of 4.5%
per annum)

Exchange rates: nominal and real effective exchange rate.
Identifying the monetary policy shocks using a reaction
function for the ECB
Ex-post available data
Estimates of forward-looking Taylor rules in the euro area
it  (1   )  (1   ) t n  (1   )  yt    it   t
Specification




R2
adj R 2
J-stat.
p(J)
[2]
0.05
-1.34
0.88
0.712
0.83
0.81
(0.143)
0.71
0.00
(10.9)
0.24
(-5.57)
0.11
(7.87)
0.05
(12.38)
0.07
-2.27
0.72
0.81
0.90
0.89
(0.168)
0.82
0.01
(6.94)
0.26
(-4.40)
0.14
(5.07)
0.05
(15.69)
0.05
-1.63
0.51
0.554
0.67
0.64
(0.164)
0.82
0.00
(14.5)
0.20
(-8.06)
0.06
(7.75)
0.08
(6.22)
0.06
-1.70
1.28
0.594
0.85
0.83
(0.101)
0.82
0.00
(19.8)
0.18
(-9.16)
0.13
(9.18)
0.113
(5.23)
[3]
 t 4
 t 6
SE
(t-statistic)
SE
(t-statistic)
[4]
 t 4 , og_qua
SE
(t-statistic)
[5]
 t 4 , ogt 1


SE
(t-statistic)
GMM. The instrument set includes lagged values (up to 4 lags) of the interest rate, inflation
and output gap. The results are not very sensitive in respect to the number of lags used as
instruments, the J-statistic supports the over-identifying restrictions implied by the model.
The standard errors were computed using the delta method. The J-statistic reported in the
table is the minimized value of the objective function, p(J), the null hypothesis that the
overidentifying restrictions are satisfied (Hansen’s J-test).
Identifying the monetary policy shocks using a reaction
function for the ECB
Ex-post available data
Additional explanatory variables and alternative specifications for Taylor rules in the euro area
it  (1   )  (1   ) t n  (1   )  yt  (1   )  xt    it   t
Specification
[4]
 t 4
xt  d (log( neer ))
[5]
 t 4
xt  d (log( reer ))
[6]
 t 4
xt  d (log( m3))
[7]
 t 4
xt  m _ gap
[8]
 t 4
irt  euribor 3M
[9]
 t 4
irt  10 y _ bond
SE
(t-statistic)
SE
(t-statistic)
SE
(t-statistic)
SE
(t-statistic)
SE
(t-statistic)
SE
(t-statistic)





R2
adj
R2
J-stat.
p(J)
0.05
-1.29
1.06
0.712
-0.01
0.87
0.85
(0.145)
0.71
0.00
(12.9)
0.20
(-6.37)
0.08
(11.9)
0.05
(12.3)
0.05
(-0.21)
0.04
-1.02
0.94
0.68
-0.02
0.88
0.86
(0.126)
0.77
0.01
(9.15)
0.26
(-3.82)
0.09
(9.69)
0.11
(5.75)
0.05
(0.35)
0.05
-1.37
0.86
0.554
-0.01
0.82
0.80
(0.140)
0.73
0.00
(11.3)
0.25
(-5.46)
0.10
(7.75)
0.08
(6.22)
0.07
(-0.13)
0.25
-0.85
0.86
0.594
-0.007
0.82
0.80
(0.140)
0.72
0.04
(5.50)
0.18
(4.73)
0.03
(25.5)
0.113
(5.23)
0.00
(-1.30)
0.05
-1.58
0.95
0.66
0.76
0.73
(0.24)
0.88
0.00
(21.5)
0.10
(-15.4)
0.06
(14.2)
0.04
(16.0)
0.75
0.73
(0.165)
0.62
-0.38 20.58
-13.7 0.97
1.78
(-0.2)
58.39
(-0.2)
85.28
(0.24)
0.08
(11.0)
-
-
Identifying the monetary policy shocks using a reaction
function for the ECB
Survey data
Sample period 1999:1-2005:4

Quarterly forecasts based only on real-time available information

Solid arguments in favor of using survey data:
policy makers;
with lags;
- they are more suitable to capture the forward-attitude of the
- variables (in particular the series for output) are only available
- data are often subject to revisions and it may take some
quarters before the final series are available.

Inflation: based on the data from the Survey of Professional Forecasters (SPF),
measured by the latest available forecast for the current year

A measure of the state of real economy: the Economic Sentiment Indicator
(ESI). This economic index appears to be more closely tied to the Governing
Council’s interest rate decisions than other variables capturing real economic
activity.
Identifying the monetary policy shocks using a reaction
function for the ECB
Survey data

ESI as a leading indicator for economic activity
The rescaled ESI gap vs. the recursive output gap
2
Carstensen and Colavecchio (2005);
Gerdesmeier and Roffia (2005);
Gerlach (2004);
Sauer and Sturm (2003).
1
0
-1
-2
1999
Studies estimating the reaction
function using survey data:
2000
2001
2002
2003
Output gap HP recursive
2004
2005
ESI gap recursive
2006
Identifying the monetary policy shocks using a reaction
function for the ECB
Survey data
Sample period 1999:1-2005:4
Estimated forward-looking Taylor rules using survey data
it  (1   )  (1   )  for  (1   )  yt    it   t
Explanatory
variables



[6]
0.007
1.16
0.50
(0.31)
(0.00)
(0.00)
1.56
0.56
-
(0.00)
(0.00)
-
-0.023
2.68
2.64
0.88
(0.89)
(0.00)
(0.00)
(0.00)
1.51
2.19
0.86
(0.00)
(0.00)
(0.00)
[7]
c
HICP_for
ESI_gap
HICP_for
ESI_gap
c
HICP_for
ESI_gap
Ir(-1)
[9]
HICP_for
ESI_gap
Ir(-1)
p-value
p-value
[8]


p-value
p-value
-

R2
Adj
R2
0.33
0.28
0.30
0.28
0.97
0.96
0.96
0.96
-
The first two specifications show the results without partial adjustment. Although this
restriction is rejected by the data, the estimates correspond to the original Taylor coefficients.
The constant term is found to be statistically insignificant, similar to the findings of
Carstensen and Colavecchio (2005).
The equation that best fits the data is considered to be [9], with an interest rate smoothing
and no constant term.
Identifying the monetary policy shocks using a reaction
function for the ECB
Survey data

Identified monetary shocks
Actual vs. Fitted interest rate & the Monetary Shocks
.05
.04
.03
.004
.02
.000
.01
-.004
-.008
1999
2000
2001
2002
2003
2004
Monetary shocks (left axis)
Actual int. rate (right axis)
Fitted int. rate (right axis)
2005
Identifying the monetary policy shocks using a reaction
function for the ECB
Survey data
Review of Taylor rule estimations for the euro area
- using survey data Study
Sample period

Carstensen and
Colavecchio (2005)
1999:1-2003:2
0.012
(0.019)
1.61
(0.01)
1.34
(0.01)
0.94
(0.00)
Gerdesmeier and
Roffia (2005) -  t 24
1999:1-2003:6
-0.84
(0.97)
2.91
2.02
(0.00) (0.00)
0.67
(0.00)
Gerdesmeier and
Roffia (2005) -  t 12
1999:1-2003:6
1.87
(0.00)
1.31
(0.00)
1.95
(0.00)
0.71
(0.00)
1999:1-2003:3
0.25
(0.41)
2.31
(0.00)
2.35
0.92
(0.01) (0.00)
Sauer and Sturm
(2003)






The results show a greater weight attached to the output gap relative to inflation, a
conclusion similar to that of the studies using ex-post data.
For some specifications, the constant term is found to be statistically insignificant.
The real time forward-looking specifications of the Taylor rule using the SPF forecasts denote
a stabilizing behavior and provide a better description of the actual behavior of the central
bank.
The transmission of shocks – a SVAR approach
Description of the variables. Quarterly data comprising:

Inflation rate (pi_ro), calculated using log deviation of the CPI from the
previous quarter;

Core inflation (core1), CPI – all items excluding administrated prices;

Real interest rate (rr_ro), the difference between BUBOR 3M and the
inflation rate;

Real GDP growth rate (d(y_ro));

Exchange rate appreciation (d(log(er))), as the log difference between
the quarterly mean of the exchange rate and that of the previous period.
The series are seasonally adjusted using TRAMO/SEATS (Demetra). The
sample period, due to data availability for the European reaction
function is 1999:1-2005:4 (28 observations). CB and Eurostat databases.
The transmission of shocks – a SVAR approach
Main three methods to identify the pure innovations:
 The recursive approach (the triangular Choleski
decomposition)
 The structural approach as advocated by Sims and
Bernanke
 The long-term restriction approach (the Blanchard and
Quah decomposition)
The transmission of shocks – a SVAR approach
The models:

Model (A1): rr_ro, er, mshock;

Model (A2): core1, er, mshock;

Model (A3): pi_ro, er, mshock;

Model (B): y_ro, rr_ro, er, mshock.
Isolating pure shocks by Choleski ordering:

Model (A1): mshock  er  rr_ro;

Model (A2): mshock  er  core1;

Model (A3): mshock  er  pi_ro.
The Choleski ordering places the monetary shocks first, reaffirming their exogeneity
towards the Romanian economic variables.
The transmission of shocks – a SVAR approach
Isolating pure shocks:
The identification scheme for Model (B)
y_ro
rr_ro
er
mshock
y_ro
1
0
0
0
rr_ro
0
1
NA
NA
er
0
0
1
NA
mshock 0
0
0
1
The Model (B) relies on an identification scheme which assumes that
contemporaneously (within a quarter), the external monetary shock
affects only the financial variables (the exchange rate and the real
interest rate) and not the real activity in Romania.
The transmission of shocks – a SVAR approach
Tests for selecting the number of lags

Model (A1): rr_ro, er, mshock;
Lag
LogL
LR
FPE
AIC
SC
HQ
0
1
209.1206
225.3001
NA
26.96576*
6.96e-12
3.86e-12*
-17.17672
-17.77501*
-17.02946
-17.18598*
-17.13765
-17.61874*
* indicates lag order selected by the criterion


Model (A2): core1, er, mshock;
Lag
LogL
LR
FPE
AIC
SC
HQ
0
1
225.3075
239.7030
NA
23.99261*
1.81e-12
1.16e-12*
-18.52562
-18.97525
-18.37836
-18.38622*
-18.48655
-18.81898*
Model (A3): pi_ro, er, mshock.
Lag
LogL
LR
FPE
AIC
SC
HQ
0
1
226.1659
275.5893
NA
82.37246*
1.68e-12
5.85e-14*
-18.59716
-21.96578*
-18.44990
-21.37675*
-18.55809
-21.80951*
The transmission of shocks – a SVAR approach
Tests for selecting the number of lags

Model (B): y_ro, rr_ro, er, mshock
Lag
LogL
LR
FPE
AIC
SC
HQ
0
1
2
3
4
299.7993
318.8971
343.5426
360.8508
381.3670
NA
30.23806
30.80686*
15.86592
11.96779
2.32e-16
1.83e-16
1.02e-16*
1.31e-16
2.18e-16
-24.64995
-24.90809
-25.62855
-25.73757
-26.11392*
-24.45360*
-23.92638
-23.86147
-23.18512
-22.77610
-24.59786
-24.64764
-25.15974
-25.06040
-25.22839*
The transmission of shocks – a SVAR approach
The impulse-response functions for (A1) model: rr_ro, er, mshock
The impulse-response functions for (A2) model: core1, er, mshock
The transmission of shocks – a SVAR approach
The impulse-response functions for (A3) model: pi_ro, er, mshock
The impulse-response functions for (B) model: y_ro, rr_ro, er, mshock
The transmission of shocks – a SVAR approach
The variance decomposition for (A1) model
Variance Decomposition of RR_RO
Variance Decomposition of MSHOCK
Variance Decomposition of ER
100
100
100
80
80
80
60
60
60
40
40
40
20
20
20
0
0
0
1
2
3
4
5
6
R R _R O
7
ER
8
9
10
1
2
3
MSH OC K
4
5
R R _R O
6
7
ER
8
9
1
10
2
3
4
5
R R _R O
MSH OC K
6
7
ER
8
9
10
MSH OC K
The variance decomposition for (A2) model
Variance Decomposition of CORE1
Variance Decomposition of ER
100
Variance Decomposition of MSHOCK
80
100
70
80
80
60
50
60
60
40
40
40
30
20
20
20
10
0
0
1
2
3
4
5
CORE1
6
7
ER
8
9
10
0
1
2
3
MSHOCK
4
5
6
CORE1
7
8
ER
9
10
1
2
MSHOCK
3
4
5
CORE1
6
7
ER
8
9
10
MSHOCK
The variance decomposition for (A3) model
Variance Decomposition of PI_RO
Variance Decomposition of ER
100
Variance Decomposition of MSHOCK
90
100
80
80
70
80
60
60
60
50
40
40
40
30
20
20
20
10
0
1
2
3
4
PI_RO
5
6
ER
7
8
9
MSHOCK
10
0
0
1
2
3
4
PI_RO
5
6
ER
7
8
9
MSHOCK
10
1
2
3
4
PI_RO
5
6
ER
7
8
9
MSHOCK
10
The transmission of shocks – a SVAR approach
The variance decomposition for (B) model
Variance Decomposition of Y_RO
Variance Decomposition of RR_RO
100
100
80
80
60
60
40
40
20
20
0
0
1
2
3
4
5
6
Shock1
Shock2
7
8
9
10
1
2
3
4
Shock3
Shock4
5
6
Shock1
Shock2
Variance Decomposition of ER
7
8
9
10
Shock3
Shock4
Variance Decomposition of MSHOCK
100
100
80
80
60
60
40
40
20
20
0
0
1
2
3
4
5
Shock1
Shock2
6
7
8
Shock3
Shock4
9
10
1
2
3
4
5
Shock1
Shock2
6
7
8
Shock3
Shock4
9
10
The transmission of shocks – a SVAR approach
VAR Granger Causality/Block Exogeneity Wald Tests
Date: 07/06/06 Time: 17:38
Sample: 1999Q1 2005Q4
Included observations: 27
Dependent variable: RR_RO
Excluded
Chi-sq
df
Prob.
ER
MSHOCK
10.88218
2.170348
1
1
0.0010
0.1407
All
11.87595
2
0.0026
Dependent variable: ER
Excluded
Chi-sq
df
Prob.
RR_RO
MSHOCK
3.389344
3.607199
1
1
0.0656
0.0575
All
5.088246
2
0.0785
Dependent variable: MSHOCK
Excluded
Chi-sq
df
Prob.
RR_RO
ER
0.244387
0.510308
1
1
0.6211
0.4750
All
0.511777
2
0.7742
Concluding remarks


The monetary shocks are isolated by estimating a reaction function for
the euro area. A more appropriate method to identify the policy shocks
is to use the information set available at the moment the decision is
made, i.e. survey data.
The empirical evidence does not support an impact of these shocks on
the internal variables.
-
-
-
The number of observations used for the estimation may be inappropriate
for the analyses of monetary transmission, knowing that the monetary
decisions affect the economy only with lags.
For the major part of the analyzed period, Romania’s exchange rate regime
was managed floating, but according to empirical findings and to IMF, it was
a mixed regime in the form of sliding band. The theoretical results for the
case of floating exchange rate may not hold if this assumption is not met.
Moreover, the capital account has not been fully liberalized and the stages
with the greatest impact on the balance of payments occurred in only in
2005. The financial openness is questionable before this period.
Concluding remarks

Not European business cycles and monetary innovations determine the
internal economic indicators, but domestic economic and political
developments.
-
-

During transition period major internal disturbances affected the Romanian
economy; these internal shocks include the effects of domestic political
conflicts, economic and financial crises, domestic policy mistakes and so on.
Apart from the obvious advantages incurred by the imminent accession, the
unpredictable effect of the European monetary policy on the Romanian
economic variables can trigger integration costs not dealt with so far.
Further research:
-
-
Alternative methods for identification of monetary policy shocks (using the
data-determined approach and the Blanchard and Quah decomposition).
The reaction function for the ECB can be obtained by employing monthly
data, using cubic splines on real GDP.
In order to test the relevance of the theme it is useful to analyze the
transmission of the identified monetary policy shocks in other countries
except Romania, namely the new EU member countries. It can also be tested
whether there is an asymmetry bween the effects of a negative and positive
monetary shock.
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