Transcript Slide 1

The Impact of External Shocks
on Emerging Countries, The
Case of Romania
MSc Student: POPA LACRAMIOARA
SUPERVISOR:Professor MOISA ALTAR
Topics

Motivation
 Objectives
 Literature review
 FAVAR approach
 The asymmetry of external shocks,(TVAR)
 Conclusions
Motivation
•Because of the perspective of the integration in the European
and Monetary Union it is important to establish a correct
assessment of the impact of the euro area shocks on the
Romanian economy
•Moreover the financial crisis reflect the fact that the monetary
authority should take into account the external disturbances
when designing the monetary policy because even if Romania
had important gains in which concern the reduction in inflation,
reduction of budget deficit and unemployment it is still small
open economy vulnerable to external disturbances originating
from more developed countries
Objectives
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This paper has to major purposes:
to establish if the external originating from the
euro area are transmitted to the Romanian
economy and their magnitude
I have considered three kind of shocks:
monetary policy shocks, demand shocks and
supply shocks
finding possible asymmetric effects of a negative
and a positive shock.
Literature review
•The study of the open economies started with the work of
Mundell(1962,1963),Dornbusch(1976), Obsfeld anf Rogoff(1995).
•Canova (2005) uses a Vector autoregressive model identified using sign
restrictions to study the effects of US shocks on several countries from the
Latin America
•Persman and Smets (2001) effects of an unexpected change in monetary
policy in the euro area using an identified VAR model alternative identification
scheme
•Dawit Senbet(2008) uses a Factor augmented VAR model on a wide range of
macroeconomic indicators for several industrialized countries. He findes that
model with factors eliminates the “price puzzle” response evident in the
standard VAR models for all countries
Econometric methodology -FAVAR
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The model consists of two blocks, one for the
Romanian economy which is named “domestic” and
one for the Euro area, which is named ‘foreign’ .
Let Yt be an M ×1 vector of observable economic
variables from the foreign block assumed to affect
the dynamics of the variables from the Romanian
economy
Additional economic information, not fully captured
by the Yt, may be relevant to modeling the dynamics
of these series. Suppose that this additional
information can be summarized by an K ×1 vector of
unobserved factors, Ft, where K is “small”.
Econometric methodology -FAVAR
 Ft 
 Ft 1 
Y   ( L) Y
  t
 t 
 t 1 
X   Ft   Yt  e
'
t
f
'
y
'
'
t
y -NxK-matrix of factor loadings
f
et' , t
Ft
Yt
Xt
-NxM-matrix of factor loadings
-vector of error terms
ν~N(0, Q)
-Kx1-vector of unobserved factors
-Mx1-vector ofobservable factors
-Nx1-informational time series
e ~ N(0, R)
2.1Transform the model into the following state-space form:
 X t   f
Y   
 t  0
y   Ft  et 
    
0  Yt  0 
 Ft 
 Ft 1 
Y   ( L) Y   t
 t
 t 1 
-measurement equation
-transition equation
  (f , y , R, ( L),Q)
Define:
X  ( X , Yt )
e  (e ,0)
R  cov(e e )
Ft '  (Ft ' , Yt ' )'
'
t
'
t
' '
'
t t
'
t
'
t
'
X t  Ft  et
-where Λ is the loading matrix from the
measurement equation
Ft  ( L) Ft 1  t
~
X T  ( X1, X 2 ,..., X T )
~
~
p ( )   p ( FT ,  )dFT
~
 p( FT , )
~
FT  ( F1, F2 ,...,FT )
~
~
p ( FT )   p ( FT ,  )d
-joint posterior density
2.2 The Gibbs sampling methodology
-it implies following three steps
1 First, choose a set of starting values for the parameters θ , say
2 Second, conditional on 
values
for ~
FT
0
~
and the data X T draw a set of
~ ~ 0
from the conditional density
p( FT X T , )
3 Third, conditional on the sampled values of
and the data, draw a set of values of the
parameters θ
1
T
T
~ ~
p( X , F )
~
FT
Say-
0
~1
FT
1. Choice
of 
0
I used the principal components method
2 Drawing from the conditional distribution
~ ~
p( FT X T , )
•It can be expressed as the product of conditional distributions of factors
at each date t as follows
T 1
~ ~
~
~
p( FT X T , )  p( FT X T , ) p( Ft Ft 1 , X t , )
t 1
•I use Kalman filter to estimate the unobservable factors
3 Drawing from the conditional distribution
~ ~1
p( X T , FT )
X t  Ft  et
Ft  ( L) Ft 1  t
1.Rgression equation
Bayes theorem
L ( , h) 
h
N
2
2
N
2
X t  Ft  et
Define:
h   1
p(, h X )  P(, h)L(, h)
h
exp[ ( X  F ) ' ( X  F )]
2
ˆ  ( F ' F ) 1 F 1 X

t
t
SSE  ( X  Fˆ ) 1 ( X  Fˆ )
L
h
N
2
2
N
2
h
ˆ ) F ' F (  
ˆ ))
exp[ ( SSE  (   
t
2
k
2
1
' 1
p()  2 h exp( (   ) R (   ) 
2
k
1
2
 h exp( (   ) ' R 1 (   )
2
K
2
 ( Rii )  h
 2
2
hs 2
exp[
]
2
2 VAR model
Ft  ( L) Ft 1  t
The Minnesota Prior – conditions
They are based on an approximation which leads to great
simplifcations in prior elicitation and computation
,
The original Minnesota prior simplifies even further by assuming Σ to
be a diagonal matrix.
-each equation of the VAR can be estimated one at a time and we can
2
2
set i  ii  si (where
is the standard OLS estimate of the error
i
variance in the ith equation and is  ii the iith element of
)
s

For the prior mean of ,  (L) Min, the Minnesota prior involves setting
most or all of its elements to zero , except for the elements
corresponding to the first own lag of the dependent variable in each
equation these elements are set to one.
-constructing diagonal elements of

so that the prior variance of parameter on k lagged j'th variable in i'th
2
2
equation equals  i / k j
Data description
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
The analysis is based on monthly data covering the period
2000M01-2010M02. The sources of data are Eurostat, The
National Bank of Romania and National Institute of Statistics.
For each of the two blocks I collected data on real activity,
inflation, money and interest rates.
For real activity, I considered data on output growth,
employment, imports and exports.
Inflation is measured including the consumer price index on
different category of goods (alimentary, nonalimentary) services,
interest rates include ROBOR on different maturities, 3M
EURIBOR and monetary policy interest rate.
Data description
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
The data were seasonally adjusted using European
Union program Demetra and were normalized
considering 2005=100.
Because the number of variables used in the model is
very high I included their description in the Appendix A of
the paper where are also specified the results of the unit
root tests
FAVAR – Estimation Results
Impulse response to a foreign monetary policy shock
hicpEUR
euribor
0.4
CSEUR
0.15
0.3
0.1
0.2
IPC
0.4
0.2
0.3
0.15
0.2
0.1
0.1
0.05
0
0
0.05
0.1
0
0
-0.1
-0.05
0
100
-0.1
0
IPCMN
100
-0.05
0
IPCMA
0.3
0
IPCSERV
0.2
0.15
0.2
100
100
ROBOR 3M
0.3
1.5
0.2
1
0.1
0.5
0
0
0.1
0.1
0.05
0
0
-0.1
-0.05
0
100
-0.1
0
crEUR
100
-0.5
0
crRON
0.05
100
0
ROBOR6M
1
100
PI
1.5
0.2
1
0
0.5
-0.2
0
-0.4
0.5
0
0
-0.05
-0.5
0
100
-0.5
0
100
-0.6
0
100
0
100
Impulse response to a foreign monetary policy shock
exp
PPIFI
0.4
0.15
0.3
0.1
0.2
0.05
0.1
0
0
-0.05
PPIUC
PPI
0.15
0.2
0.15
0.1
0.1
0.05
-0.1
0.05
0
-0.1
0
100
UNEMPL
0.08
0.06
0.04
0.02
0
-0.02
0
100
0
-0.05
0
100
-0.05
0
100
0
10
Impulse response to a foreign supply shock
prod ndEU
euribor
hicpEUR
i
0.04
0.4
0.03
0.3
0.02
0.2
0.01
0.1
0
0
-0.01
-0.1
0
100
0.06
0.04
0.04
0.02
0.02
0
0
-0.02
0
IPC
100
-0.02
0
IPCMN
0.06
0.06
0.04
0.04
0.02
0.02
CSEUR
0.06
100
0
IPCMA
10
IPCSERV
0.04
0.06
0.03
0.04
0.02
0.02
0.01
0
0
-0.02
-0.02
0
100
0
0
-0.01
0
ROBOR 3M
100
-0.02
0
crRON
100
0
ROBOR6M
PI
3
0.6
3
0.15
2
0.4
2
0.1
1
0.2
1
0.05
0
0
0
0
-1
-0.2
0
100
-1
0
100
10
-0.05
0
100
0
10
Impulse response to a foreign supply shock
exp
PPIFI
0.4
0.15
0.3
0.1
0.2
0.05
PPIUC
PPI
0.15
0.2
0.15
0.1
0.1
0.05
0.1
0
0
-0.05
-0.1
0.05
0
-0.1
0
100
UNEMPL
0.08
0.06
0.04
0.02
0
-0.02
0
100
0
-0.05
0
100
-0.05
0
100
0
10
unempl U
Impulse response to a foreign demand shock
euribor
hicpEUR
E
0.02
0
0.6
0.06
0.4
0.04
0.2
0.02
CSEUR
0.06
0.04
-0.02
0.02
-0.04
0
0
-0.06
-0.08
0
-0.2
0
100
-0.02
-0.02
0
IPC
100
-0.04
0
IPCMN
0.06
0.06
0.04
0.04
0.02
0.02
100
0
IPCMA
10
IPCSERV
0.04
0.06
0.04
0.02
0.02
0
0
0
-0.02
-0.02
-0.04
0
-0.02
-0.04
0
100
-0.04
0
ROBOR 3M
100
0.6
0.1
0.4
0
0.2
-0.04
0
crRON
0.2
-0.02
100
0
ROBOR6M
0.3
0.2
PI
0.1
0.05
0.1
0
0
-0.1
0
-0.2
-0.2
-0.1
-0.2
10
-0.05
-0.1
Impulse response to a foreign demand shock
exp
PPIFI
0.4
0.3
PPIUC
0.1
0.15
0.05
0.1
0
0.05
PPI
0.2
0.15
0.2
0.1
0.1
0.05
-0.05
0
-0.1
0
-0.1
0
100
UNEMPL
0.02
0.01
0
-0.01
-0.02
0
100
0
-0.05
0
100
-0.05
0
100
0
10
2.The asymmetry of external shocks,(TVAR)
A general TAR model, that permits the existence of two regimes
and more than one lag, may be written as :
 m1  A1 ( L) yt  1t
yt  
m2  A2 ( L) yt   2t
if
qt  c
if
qt  c
yt  (m1  A1 (L) yt 1  1t )I (qt  c)  (m2  A2 (L) yt 1   2t )(1  I (q  c))
-where I() is an indicator function which takes the value of 1 if the logical
statement is satisfied and 0 otherwise
Variables : HICP,EUR/RON exchange rate, the consumer price index, 3M ROBOR,
volume of loans in national currency, volume of loans in devises,
the index of industrial production
TVAR-Empirical results
Asymmetric effects of monetary policy shocks
8
70
400
60
300
6
50
4
200
40
100
30
2
0
20
0
1
1
11
21
31
41
51
-10
-4
1
11
21
31
41
51
-20
IFR_CS_EURS1
IFR_CS_EURS2
21
31
41
51
-200
0
-2
11
-100
10
-300
-400
IFR_PI S1
IFR_PI S2
IFR_CPI S1
IFR_CPI S2
•The reduction inflation in the euro area can be explained by a reduction in the real activity
•To stimulate the economy, the Central Bank reduces the monetary policy interest rate and the
demand for the national currency will increases, determinating an appreciation of the national
currency.
•The difference between the response of the exchange rate to a positive shock and a negative
shock can be explained by the opportunistic behavior of importers who try to take advantage of
the decrease in European prices causing the appreciation of the national currency
•The difference in response in consumer prices can be explained by the behavior of producers
who try to maintain their market share while in the case of depreciation firms absorb a part of the
inflationary impact.
•The response of the industrial production to european monetary policy shock is much bigger in
the case of negative shock. An explenation for this may be the fact that usually, negative shocks
have bigger effects on emerging markets, because of their dependencies on exports in covering
the current account deficits
TVAR(positive and negative monetary policy shocks- Probabilities of the two
regimes 􀜛
TVAR, 2000 (3) - 2010 (1)
5
0
L_HICP_EU
DL_CPI
DL_CRNGR
DL_PI
DLCS_EUR
DROBOR3M
DL_CRNGV
-5
-10
1.0
2001
2002
2003
Probabilities of Regime 1
2004
2005
2006
2007
2008
2009
2010
2001
2002
2003
Probabilities of Regime 2
2004
2005
2006
2007
2008
2009
2010
2004
2005
2006
2007
2008
2009
2010
0.5
1.0
0.5
2001
2002
2003
-it presents the periods in which each of the two regimes occurred.
Both regimes includes periods of small changes in HICP
TVAR-Empirical results
Asymmetric effects of demand shocks
20
350
15
300
20
10
10
250
0
5
0
-5
1
200
1
11
21
31
41
51
-10
11
21
31
41
51
-10
150
100
-20
50
-30
-15
-20
0
-25
IFR_CS_EURS1
IFR_CS_EURS2
1
11
21
IFR_CPI S1
31
41
51
IFR_CPI S2
-40
IFR_PI S1
IFR_PI S2
•An increase in unemployment rate causes a reduction in the real activity of the euro
area,so the Central Bank will adopt an expansionary monetary policy reducing the
interest rates.
•An increase of the unemployment rate determines a reduction in the
real activity from the euro area. The EURO/RON exchange rate increases and this
can be explained by the reduction of demand for currencies of the emerging
countriesinvestors preferring to buy safer currencies.
•The second regime reflects that a reduction in the unemployment rate determines a decrease
of the exchange rate. This may happen because of an increase in real activity, leading to an
Increse imports which results in the increase of exports for the national economy
TVAR(positive and negative demand shocks- Probabilities of the two regimes
TVAR, 2000 (3) - 2010 (1)
5
0
DL_UNEMPL_EUR
DL_CPI
DL_CRNGR
DL_PI
DLCS_EUR
DROBOR3M
DL_CRNGV
-5
-10
1.0
2001
2002
Probabilities of Regime 1
2003
2004
2005
2006
2007
2008
2009
2010
2001
2002
Probabilities of Regime 2
2003
2004
2005
2006
2007
2008
2009
2010
2003
2004
2005
2006
2007
2008
2009
2010
0.5
1.0
0.5
2001
2002
Conclusions
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I showed that external innovations play an important role
for the evolution of macro-economy variables
External shocks propagate quickly to our economy and
their effects are felt for a long period of time, more than
one year
Comparing the three shocks we see that the effect of the
demand shocks is lower then that of the monetary policy
shocks and is transmitted in the economy for a shorter
period of time.
An explanation for this may be the fact that financial
variables react more quickly to an innovation then the
variables referring to real activity for example
Asymmetric effects manifests for all the shocks
considered
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Kalman filter for
Prediction
~
p( FT X T , )
Ft t 1  ( L) Ft 1 t 1
X t  Ft  et
Ft  ( L) Ft 1  t
Pt t 1  ( L) Pt 1 t 1( L) '  Q
t t 1  X t  X t t 1  X t   t Ft t 1
f t t 1  t Pt t 1't  R
Updating
Ft t  Ft t 1  K tt t 1
Pt t  Pt t 1  K t  t Pt t 1
Kt  Pt t 1't f t t 11
is the Kalman gain
Thank You!