Exchange Rate Pass-Through into Inflation in Romania

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Exchange Rate Pass-Through into Inflation in Romania

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Title: Exchange Rate Pass-Through into Inflation in Romania

1
Exchange Rate Pass-Through into Inflation in
Romania
The Academy of Economic Studies Doctoral School
of Finance and Banking

MSc student Ciurila Nicoleta
Coordinator Professor Moisa Altar

Bucharest, July 2006
2
Dissertation paper outline

The importance of the exchange rate pass-through
The aims of the paper
Empirical studies concerning exchange rate
pass-through
The Data
The VAR approach
The single equation approach
Conclusions
References

3
The importance of exchange rate pass-through

Exchange rate pass through - the percentage
change in local currency import prices resulting
from a one percent change in the exchange rate
between the importing and the exporting
countries (Goldberg and Knetter (1997)) the
change in import prices is passed to some extent
into producer and consumer prices
Taylor (2000)-importance in the conduct of
monetary policy because of its impact on
inflation forecasts.
Countries that experience high exchange rate
pass-through tend to put more emphasis on
exchange rate in the conduct of their monetary
policy- especially emerging and transition
countries.
Pass through has an important role in EU acceding
countries which will face additional constraints
because of ERMII criteria.
Edwards(2006)- a high pass-through into
nontradable goods prices reduces the
effectiveness of the exchange rate, while a high
pass through into tradable goods prices will
enhance its effectiveness.

4
The aims of the paper

To quantify the the size and speed of the
exchange rate pass through into inflation
To test whether the size of the pass through is
dependant on the currency chosen as the base
currency
To determine the variables which account for
inflation variability
To determine whether the size of the pass
through has declined in time
to test if exchange rate volatility influences
the size of the pass through
to check for asymmetries in the exchange rate
pass-through.

5
Empirical studies concerning exchange rate
pass-through

Single equation method all studies before 1995,
Goldberg and Knetter (1997), Campa and Minguez
(2002), Campa, Goldberg and Minguez (2005),
Elkayam (2004), Edwards (2006).
VAR method and cointegration analysis Kim
(1998), McCarthy (2000), Hahn(2003), Leigh and
Rossi(2002), Gueorguiev(2003), Billmeier and
Bonato (2002), Coricelli, Jazbec, Masten (2004),
Huefner and Schroeder (2002), Arnostova and
Hurnik (2004).
Structural models usually developed by central
banks Quartely Projection Models Gagnon Ihrig
(2004).

6
Adjusting the empirical analysis for the
characteristics of the Romanian economy

including a central bank reaction function in
the model may
prove useless as NBR has only recently adopted
the interest rate as operating target
import prices are only available on a quarterly
basis, so an analysis using
these prices isnt possible in a model using
monthly data
it would be more useful to replace CPI based
inflation with the inflation
computed using the CORE1 price index

7
The Data

Monthly data series for the period of
2000M12006M2
The Gap of the real Production Index (GAP_IPR)
obtained by deflating the PI with the PPI and
then applying a Hodrick-Prescott filter - I(0)
The first difference of the log RON/EUR exchange
rate (DEURM) I(0)
The first difference of the log RON/USD exchange
rate (DUSDM) I(0)
The first difference of a basket currency
computed as 65 EUR and 35 USD (weights given by
the proportion of the imports denominated in EUR,
respectively in USD) (DBASKET) I(0)
The first difference of the log PPI seasonally
adjusted using the Tramo-Seats procedure in
Demetra (INFL_PPI_SA)- I(0)
The first difference of the log Core1 index
seasonally adjusted using the Tramo-Seats
procedure in Demetra (INFL_CORE1_SA)- I(0)
The first difference of the log HICP seasonally
adjusted using the Tramo-Seats procedure in
Demetra (DHICP) I(0)
The first difference of the log broad money
aggregate (DM2) I(0)
For alternative specifications the monetary
policy interest rate, the first difference of the
log of gross nominal wage

8
The VAR approach
Variables
1. Endogenous real industrial production index gap, first difference of the log of the exchange rate (appreciation/depreciation of the RON), first difference of the log of PPI, first difference of the log of CORE1 index
2. Endogenous real industrial production index gap, first difference of the log of the exchange rate (appreciation/depreciation of the RON), first difference of the log of PPI, first difference of the log of CORE1 index , first difference of the log of broad money aggregate.
3. Endogenous real industrial production index gap, first difference of the log of the exchange rate(appreciation/depreciation of the RON), first difference of the log of PPI, first difference of the log of CORE1 index , first difference of the log of broad money aggregate. Exogenous first difference of the log of HICP
4. Endogenous first difference of the log of HICP, first difference of the log of the exchange rate(appreciation/depreciation of the RON), first difference of the log of PPI, first difference of the log of CORE1 index , first difference of the log of broad money aggregate.
9
Results of VAR approach

Lag length criteria suggests for each model a
specification including 1 lag
The VAR approach uses the impulse response
functions to analyse the pass through
Speed of pass through number of periods after
which the PPI based inflation and Core1 inflation
revert to their long run levels
Size of pass through
Where Pt is the cumulative response of the
inflation to a standard deviation shock in the
exchange rate innovation
Et is the cumulative response of the
exchange rate to a standard deviation shock in
the exchange rate innovation.
Short run pass through t1
Long run pass through . In
practice, we stop when the ratio stabilises.

10
The speed of pass through
RON/EUR exchange rate
The initial shock in the exchange rate works
through the system in about 12 periods if we
consider confidence intervals even less
RON/USD exchange rate
11
The size of pass through
RON/EUR exchange rate
Model 1 Model 2 Model 3 Model 4
Short run pass-through into PPI 0.22 0.18 0.17 0.16
Long run pass-through into PPI 0.45 0.40 0.42 0.37
Short run pass-through into CORE1 0.10 0.07 0.06 0.07
Long run pass-through into CORE1 0.37 0.32 0.33 0.30
AIC -24.32 -29.96631 -29.61585 -32.86004
Log likelihood 919.89 1090.88 1101.171 1212.961
Determinant residual covariance (dof adj.) 2.48E-16 7.35E-20 5.96E-20 2.48E-21
12
The size of pass through

the pass-through in PPI based inflation is
consistently greater than in Core1 inflation.
This is due to
the size of the pass through depends on the
weight of the goods and services in the price
index that are affected by the exchange rate
shock
the number of stages that a shock has to pass is
also important because at each stage the
pass-through is incomplete
adding the first difference of the broad money
aggregate seriously improves the log likelihood
and the Akaike Information Criteria also
decreases
adding the nominal gross wage to the model or
removing it has no impact on the estimation
including the monetary policy rate as endogenous
variable very weak responses of both PPI and
CORE1 inflation to any shocks, high persistence
of the monetary policy interest rate, all the
coefficients in the monetary policy interest rate
equation are highly insignificant with the sole
exception of the monetary policy interest rate
itself
the ordering of the variables is an issue of
discussion, especially the position of DM2 in the
ordering of the variables-reordering the
variables proves insignificant for the speed and
size of pass through but significant for variance
decomposition

13
The size of pass through
RON/USD exchange rate
Model 1 Model 2 Model 3 Model 4
Short run pass-through into PPI 0.16 0.10 0.09 0.08
Long run pass-through into PPI 0.35 0.33 0.30 0.28
Short run pass-through into CORE1 0.07 0.05 0.03 0.03
Long run pass-through into CORE1 0.28 0.25 0.21 0.21
AIC -24.21 -29.24861 -29.41548 -32.86004
Log likelihood 916.07 1082.950 1093.957 1212.961
Determinant residual covariance (dof adj.) 2.75E-16 9.17E-20 7.29E-20 2.48E-21
14
The size of pass through
RON/basket exchange rate
Model 1 Model 2 Model 3 Model 4
Short run pass-through into PPI 0.26 0.17 0.16 0.16
Long run pass-through into PPI 0.45 0.37 0.40 0.37
Short run pass-through into CORE1 0.09 0.10 0.09 0.07
Long run pass-through into CORE1 0.35 0.25 0.28 0.30
AIC -24.82 -30.4291 -30.4922 -32.86
Log likelihood 955.18 1135.235 1142.472 1212.961
Determinant residual covariance (dof adj.) 1.21E-16 2.07E-20 1.83E-20 2.48E-21
15
The size of pass through-Conclusions

the RON/USD exchange rate pass-through is
systematically smaller than the RON/EUR exchange
rate pass-through. This can be explained by the
fact that the estimation sample contains a longer
period of EUR reference on the FOREX market
the pass-through coefficients for the basket are
somehow in between those previously obtained, but
a bit biased towards the estimates obtained for
the RON/EUR exchange rate.
The model that exhibits the most economically
consistent impulse response functions and has the
highest log likelihood and the lowest AIC is
model 4, followed by model 3 we can test
alternative specifications of the model using the
block-causality test

16
Variance Decomposition for CORE1 inflation
Period GAP_IPR DEURM INFL_PPI_SA INFL_CORE1_SA
1 8.369197 10.48878 2.64847 78.49355
5 5.797656 25.88497 9.140125 59.17724
10 5.558193 26.94406 9.326178 58.17157
Model 1
Model 2
Period GAP_IPR DEURM INFL_PPI_SA INFL_CORE1_SA DM2
1 1.245019 6.05501 3.720254 88.97972 0
5 1.376158 20.72329 10.2058 63.93476 3.759999
10 1.546918 21.14246 10.86377 62.55058 3.896274
Period GAP_IPR DEURM INFL_PPI_SA INFL_CORE1_SA DM2
1 1.541631 4.464166 2.154076 91.84013 0.000000
5 5.283411 25.02377 4.900803 58.29532 6.496696
10 6.894712 25.25839 5.269784 55.98049 6.596625
Model 3
Model 4
Period DHICP DEURM INFL_PPI_SA INFL_CORE1_SA DM2
1 10.63268 5.409175 1.493547 82.46460 0.000000
5 19.39071 20.13060 4.927478 53.31920 2.232009
10 19.75258 20.65724 5.171728 52.11691 2.301542
17
Variance Decomposition for CORE1 inflation
Remarks

high persistence of CORE1 inflation in all
models, because the most important variable in
explaining its variance, even after 10 periods,
is the CORE1 inflation itself
The second most important variable that explains
the variance of CORE1 inflation is the movements
in the exchange rate
Third most important variable is euro zone
inflation
Using an alternative specification of the VAR
(DM2 comes immediately after the supply and
demand shocks ), we obtain that DM2 has a greater
explanatory power for the inflation reaching 10
after 10 periods
the importance of the exchange rate movement is
lower in case of the alternative specification

18
The stability of pass through coefficients
Recursive estimation
Rolling window estimation
Recursive estimation starting sample 44
observations 30 recursive estimations Rolling
window fixed sample 54 observations 20
windows Clear evidence for declining pass-through
coefficients Taylor(2000)- countries with
decreasing rate of inflation also experience a
decline in the size of the pass through
19
Cointegration Analysis

First specification for cointegration analysis
LEURM, LPPI, LCORE1 one cointegrating equation
the statistic of LEURM in the cointegrating
vector highly unsignificant

Second specification for cointegration analysis
LCORE1, LPPI, LEURM, LHICP one cointegrating
equation the coefficients in the cointegration
equation statistically significant, but
economically incorrect

Unrestricted Cointegration Rank Test (Trace) Unrestricted Cointegration Rank Test (Trace) Unrestricted Cointegration Rank Test (Trace) Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.
None 0.615049 113.2271 63.87610 0.0000
At most 1 0.267612 42.58387 42.91525 0.0539
At most 2 0.151597 19.53692 25.87211 0.2503
At most 3 0.094812 7.371327 12.51798 0.3074
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level Trace test indicates 1 cointegrating eqn(s) at the 0.05 level Trace test indicates 1 cointegrating eqn(s) at the 0.05 level Trace test indicates 1 cointegrating eqn(s) at the 0.05 level Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
1 Cointegrating Equation(s) 1 Cointegrating Equation(s) Log likelihood 1089.331
Normalized cointegrating coefficients (standard error in parentheses) Normalized cointegrating coefficients (standard error in parentheses) Normalized cointegrating coefficients (standard error in parentheses) Normalized cointegrating coefficients (standard error in parentheses) Normalized cointegrating coefficients (standard error in parentheses)
LCORE1 LIPP LHICP LEURM _at_TREND(98M02)
1.000000 -1.048809 11.94045 0.277874 -0.014897
(0.17945) (2.34035) (0.10458) (0.00321)
-5.84457 5.3472 2.65704 -4.6408
21
The single equation approach
In order to allow for asymmetries I added another
term in each of the above equations
First specification
Second specification
Where app is a dummy variable that selects
depreciations of the domestic currency above a
certain threshold
22
The single equation approach -results
E views specification estimation method
Seemingly Unrelated Regressions
d(lipp)c(11)c(12)deurmc(13)d(lipp(-1))c(14)
d(lhicp) d(lcore1)c(21)c(22)deurmc(23)d(lcore
1(-1))c(24)d(lhicp)
Coefficient t-Statistic Prob.
C(11) 0.008150 4.157155 0.0001
C(12) 0.233176 4.724145 0.0000
C(13) 0.368238 4.096207 0.0001
C(14) 0.300603 0.751490 0.4536
C(21) 0.002937 2.304802 0.0226
C(22) 0.125441 4.012402 0.0001
C(23) 0.623906 8.516181 0.0000
C(24) 0.460814 1.827694 0.0697

The coefficients of Euro zone inflation are
statistically insignificant this may be because
it influences our economy with a number of lags
The correlation between resulting residuals very
low 0.1039
No autocorrelation in the residuals

23
The single equation approach -results
Short run and long run pass through coefficients
Short run pass through Long run pass through
0.23 0.37
0.13 0.33
-The results are very similar to those obtained
through the VAR estimation in case of model 4.
-lower pass through into CORE1 inflation than in
PPI based inflation
24
The single equation approach the stability of
the coefficients
Short run pass though
Long run pass though
Using the rolling window technique the following
results are obtained -The short run pass through
coefficients fluctuate across the sample with the
pass through into PPI based inflation ranging
between 0.22 and 0.25 and with the pass through
into CORE1 inflation ranging between 0.11 and
0.07 -The long run pass through coefficients are
clearly far from stable and seem to be
systematically decreasing. The pass through into
PPI based inflation ranges between 0.41and 0.24 ,
while the pass through into CORE1 inflation
ranges between 0.33 and 0.17
25
The single equation approach Including the
volatility
E views specification
d(lipp)c(11)c(12)deurmc(13)d(lipp(-1))c(14)
d(lhicp)c(15)volatility d(lcore1)c(21)c(22)de
urmc(23)d(lcore1(-1)) c(24)d(lhicp)
c(25)volatility
The series of monthly exchange rate volatility
starting from daily appreciation/ depreciation of
the RON with respect to EUR a rolling GARCH(2,1)
model was fitted on the daily data. Monthly
variance was retrieved by adding daily variances
as the covariance term isnt statistically
significant.
Garch estimation for the whole sample
Coefficient Std. Error z-Statistic Prob.
C 0.000339 9.71E-05 3.492009 0.0005
Variance Equation Variance Equation
C 2.41E-07 8.28E-08 2.912080 0.0036
RESID(-1)2 0.186093 0.015232 12.21741 0.0000
GARCH(-1) 0.444902 0.091335 4.871127 0.0000
GARCH(-2) 0.383620 0.082890 4.628054 0.0000
26
The single equation approach Including the
volatility
Estimation results
Coefficient Std. Error t-Statistic Prob.
C(11) 0.006609 0.002126 3.109263 0.0023
C(12) 0.205581 0.050042 4.108210 0.0001
C(13) 0.302484 0.096210 3.143991 0.0021
C(14) 0.296789 0.402516 0.737335 0.4622
C(15) 3.761433 1.785334 2.106851 0.0370
C(21) 0.002336 0.001324 1.764566 0.0799
C(22) 0.116989 0.031967 3.659648 0.0004
C(23) 0.518972 0.089513 5.797749 0.0000
C(24) 0.486294 0.255781 1.901215 0.0594
C(25) 2.630606 1.313633 2.002543 0.0472
Pass through coefficients
Short run pass through Long run pass through
0.21 0.29
0.12 0.24
27
The single equation approach testing for
appreciation asymmetry
E views specification
d(lipp)c(11)c(12)deurmc(13)d(lipp(-1))c(14)
d(lhicp)c(15)abs(deurm) d(lcore1)c(21)c(22)de
urmc(23)d(lcore1(-1)) c(24)d(lhicp)
c(25)abs(deurm)
Coefficient Std. Error t-Statistic Prob.
C(11) 0.009777 0.002380 4.108400 0.0001
C(12) 0.284681 0.065638 4.337105 0.0000
C(13) 0.354657 0.089733 3.952357 0.0001
C(14) 0.241867 0.399639 0.605215 0.5460
C(15) -0.101853 0.086722 -1.174471 0.2422
C(21) 0.002896 0.001511 1.916673 0.0573
C(22) 0.123666 0.041500 2.979914 0.0034
C(23) 0.622929 0.073332 8.494651 0.0000
C(24) 0.463354 0.254482 1.820770 0.0708
C(25) 0.003826 0.055065 0.069472 0.9447
The two coefficients allowing for appreciation
asymmetry arent statistically significant.
28
The single equation approach testing for
asymmetry
E views specification
d(lipp)c(11)c(12)deurmc(13)appdeurmc(14)d(
lipp(-1)) c(15)d(lhicp) d(lcore1)c(21)c(22)de
urmc(23)appdeurmc(24)d(lcore1(-1))
c(25)d(lhicp) Where app is a dummy variable
taking the value of 1 for RON appreciation and 0
for RON depreciation.

The result of the above estimation is also
inconclusive. In order to examine whether there
is another threshold for the movement of the
exchange rate, the following estimation was
performed
The interval between the maximum appreciation and
the maximum depreciation of the RON was split
into equal intervals of 0.001
A dummy variable d1 was constructed for deurmgta,
where a represents each value of the interval
The above specification was estimated each time
replacing app with d1
The coefficients and the corresponding
t-statistics were saved in a matrix

29
The single equation approach testing for
asymmetry
The two t statistics have the biggest absolute
value (that means that they are the most
significant) at point 0.022287, so one could
assume that this is the threshold value for the
exchange rate change. However, further
investigations must be performed using the
methodology of Tsay(1998), Hansen(2000),
Alessandrini(2003) and Arbatli (2005).
30
Conclusions

The speed of pass-through an initial shock in
the exchange rate movement completely works
through the economy and is passed into producer
and consumer prices in 12 months
The size of the pass through varies across the
models and across the exchange rates used. The
RON/EUR exchange rate pass through into producer
prices varies between 0.37 and 0.45 depending on
the VAR model, while the pass through into
consumer prices varies between 0.30 and 0.37.
The RON/USD exchange rate pass-through is
consistently lower than the RON/EUR, ranging
between 0.28 and 0.35 for producer prices, and
between 0.21 and 0.28 for consumer prices
The basket exchange rate pass-through
coefficients are for each model found to be
between the coefficient of the RON/EUR
pass-through and the RON/USD pass-through
Variance decomposition The percent of the
variance explained by different variables varies
across models and for model 4 it also varies
across alternative orderings of the variables. It
is clear however, that the determinants of
inflation are the following inflation itself
(78-90), the exchange rate movements(21-27),
HICP inflation (19) and the variation of the
broad monetary aggregate (2-10).

31
Conclusions

Using both recursive and the rolling window
estimation sufficiently clear evidence was found
that the pass through has declined gradually
The results of the single equation approach are
consistent with the ones obtained through the VAR
method the long run pass through into producer
prices is equal to 0.37 while the long run pass
through into consumer prices is equal to 0.30
The pass through coefficients computed using the
single equation approach were also checked for
stability using the rolling window approach the
conclusion is the same pass through in Romania
seems to have decreased in the last period.
Taking into account exchange rate volatility
proved in statistically significant in both
equations and changes the pass through
coefficients The long run pass through into
producer prices becomes equal to 0.29 while the
long run pass through into consumer prices
changes to 0.24.
Including the appreciation of the exchange rate
as a distinct explanatory variable in both
equations made no difference - the coefficients
are not significant - there probably is no
asymmetry around the zero value of the exchange
rate change.
The investigation for a non-zero threshold of the
exchange rate change showed as marginally
significant a 0.022287 depreciation as a
threshold value

32
References
Amato, J., Filardo, A., Galati,G., von Peter,G.,
Zhu, F. (2005) Research on exchange rates and
monetary policy an overview, BIS Working paper
no.179 Arbatli, E. (2003) Exchange Rate Pass
Through in Turkey Looking for Asymmetries,
Central Bank Review, vol. 3, issue 2, pages
85-124 Brooks, C. (2002) Introductory
Econometrics for Finance , Cambridge University
Press Billmeier, A., Bonato, L. (2002) Exchange
Rate Pass-Through and Monetary Policy in
Croatia, IMF Working Paper, 109/2002 Caner, M.,
Hansen, B. (2004) Instrumental Variable
Estimation of a Threshold Model, Econometric
Theory, 20, 813843 Coricelli, F., Jazbec B.,
Masten I. (2004) Exchange Rate Pass-Through in
Acceding Countries, European University
Institute Working Paper no. 2004/16 Choudhri E.
U., Faruqee H., and Hakura D. (2002) Explaining
the Exchange Rate Pass-Through in Different
Prices, IMF Working Paper, 224/2002 Campa, J.M,
Gonzalez Minguez, J.M. (2002) Differences in
Exchange Rate Pass-Through in the Euro Area,
IESE Working Paper No. D/479 Christiano, L.,
Eichenbaum, M., Evans, C. (1996) Sticky Price
and Limited Participation Models of Money A
Comparison, NBER Working Paper no. 5804 Campa,
J.M, Goldberg, L., Gonzalez Minguez , J.M.
(2005) Exchange-Rate Pass-Through To Import
Prices In The Euro Area, NBER Working Paper no.
11632 Edwards, S. (2006) The Relationship
Between Exchange Rates And Inflation Targeting
Revisited, NBER Working Paper no. 12163 Enders,
W.(2004) Applied Time Series Econometrics,
John Wiley Sons
33

Engle, C., Devereux M. (2002)Exchange Rate
Pass-Through, Exchange Rate Volatility, and
Exchange Rate Disconnect, NBER working Paper no.
9568
Gagnon, J., Ihring, G. (2002) Monetary Policy
and Exchange Rate Pass-Through, Board of
Governors of the Federal Reserve System,
International Finance Discussion Paper No.
2001/704.
Goldberg, P., Knetter, M (1997) Goods Prices
and Exchange Rates What have we learned?, NBER
Working Paper no. 5862
Gueorguiev, N. (2003) Exchange rate pass
through in Romania, IMF Working Paper, 130/2003
Hahn, E. (2003) Pass Through of External Shocks
to Euro Area Inflation, ECB Working Paper,
no.243
Hufner, F.P, Schroder, M. (2002) Exchange Rate
Pass Through to Consumer Prices A European
Perspective, Centre for European Economic
Research Discussion Paper no. 02-20
Leigh, D., Rossi M.(2002) Exchange Rate
Pass-Through in Turkey, IMF Working Paper no.
2002/204
McCarthy, J. (2000)Pass-Through of Exchange
Rates and Import Prices to Domestic Inflation in
Some Industrialized Economies, Federal Reserve
Bank of New York Working Paper
Rowland, P. (2003) Exchange Rate Pass-Through
to Domestic Prices The Case of Colombia Banco
de la Republica de Colombia
Sarno, L., Taylor, M. (2002) The Economics of
Exchange Rates, Cambridge University Press
Sekine, T. (2006) Time-varying exchange rate
pass-through experiences of some industrial
countries, BIS Working Paper no 202

Taylor,J.B. (2000) Low Inflation, Pass-Through,
and the Pricing Power of Firms European Economic
Review vol. 44, issue 7, pages 1389-1408
Tsay, R (1998) Testing and Modeling
Multivariate Threshold Models, Journal of the
American Statistical Association , 94, 1188-1202.
Warne, A (1993) A Common Trends Model
Identification, Estimation and Inference,
Seminar Paper No. 555, IIES, Stockholm University

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