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The Academy of Economic Studies Doctoral School of Finance and Banking. Exchange Rate Pass-Through into Inflation in Romania. MSc student Ciuril ă Nicoleta Coordinator Professor Moisă Altăr. Bucharest, July 2006. The importance of the exchange rate pass-through The aims of the paper - PowerPoint PPT Presentation
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Exchange Rate Pass-Through into Exchange Rate Pass-Through into Inflation in RomaniaInflation in Romania
MSc student Ciurilă NicoletaCoordinator Professor Moisă Altăr
The Academy of Economic Studies
Doctoral School of Finance and Banking
Bucharest, July 2006
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
The importance of exchange rate pass-throughThe 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.
The aims of the paperThe 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.
Empirical studies concerning exchange rate Empirical studies concerning exchange rate pass-throughpass-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).
Adjusting the empirical analysis for the Adjusting the empirical analysis for the characteristics of the Romanian economycharacteristics of the Romanian economy
• including a central bank reaction function in the model mayprove 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 isn’t 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
The DataThe Data
Monthly data series for the period of 2000M1:2006M2The 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
The VAR approachThe 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.
Results of VAR approachResults of VAR approachLag 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: t=1
Long run pass through: . In practice, we stop when the ratio stabilises.
t
tt E
PPT
t
The speed of pass throughThe speed of pass throughRON/EUR exchange rate
Model1Model1 Model2Model2 Model3Model3
Model1Model1 Model2Model2 Model3Model3
RON/USD exchange rate
The initial shock in the exchange rate works through the system in about 12 periods – if we consider confidence intervals even less
The size of pass throughThe size of pass through
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
RON/EUR exchange rate
• 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
The size of pass throughThe size of pass through
The size of pass throughThe size of pass through
RON/USD exchange rateModel 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
PPI0.35 0.33 0.30 0.28
Short run pass-through into
CORE10.07 0.05 0.03 0.03
Long run pass-through into
CORE10.28 0.25 0.21 0.21
AIC-24.21 -29.24861 -29.41548 -32.86004
Log likelihood916.07 1082.950 1093.957 1212.961
Determinant residual covariance (dof
adj.)2.75E-16 9.17E-20 7.29E-20 2.48E-21
The size of pass throughThe size of pass through
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
RON/basket exchange rate
• 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
The size of pass through-ConclusionsThe size of pass through-Conclusions
Variance Decomposition for CORE1 Variance Decomposition for CORE1 inflationinflation
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 1Model 1 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 2Model 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
Model 3Model 3
Model 4Model 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
Variance Decomposition for CORE1 Variance Decomposition for CORE1 inflation –Remarks 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
The stability of pass through The stability of pass through coefficientscoefficients
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
pass through ppi
pass thorugh core1
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
pass through ppi
pass thorugh core1
Recursive estimationRecursive estimation Rolling window estimationRolling 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
Cointegration AnalysisCointegration AnalysisFirst specification for cointegration analysis:
LEURM, LPPI, LCORE1: one cointegrating equation – the statistic of LEURM in the cointegrating vector highly unsignificant
Unrestricted Cointegration Rank Test (Trace)
Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.469373 57.49043 29.79707 0.0000
At most 1 0.133030 10.59696 15.49471 0.2376
At most 2 0.000451 0.033389 3.841466 0.8550
Trace test indicates 1 cointegrating eqn(s) at 0.05 level
1 Cointegrating Equation(s): Log likelihood 734.8356
Normalized cointegrating coefficients (standard error in parentheses)
LCORE1 LIPP LEURM
1.000000 -0.553109 -0.014964
(0.05064) (0.06735)
[-10.922] [-0.22218]
Cointegration AnalysisCointegration Analysis
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)
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
1 Cointegrating Equation(s): Log likelihood 1089.331
Normalized cointegrating coefficients (standard error in parentheses)
LCORE1 LIPP LHICP LEURM@TREND(98M0
2)
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]
The single equation approachThe single equation approach
Short run pass through coefficients: 22ˆandˆ
Long run pass through coefficients:
4
2
4
2
ˆ1
ˆand
ˆ1
ˆ
In order to allow for asymmetries I added another term in each of the above equations:
tttttt EPPIHICPEPPI logloglogloglog 514321
tttttt EappPPIHICPEPPI log**loglogloglog 514321
First specification
Second specification
Where app is a dummy variable that selects depreciations of the domestic currency above a certain threshold
The single equation approach -resultsThe single equation approach -results
E views specification – estimation method Seemingly Unrelated Regressions
d(lipp)=c(11)+c(12)*deurm+c(13)*d(lipp(-1))+c(14)*d(lhicp)d(lipp)=c(11)+c(12)*deurm+c(13)*d(lipp(-1))+c(14)*d(lhicp)d(lcore1)=c(21)+c(22)*deurm+c(23)*d(lcore1(-1))d(lcore1)=c(21)+c(22)*deurm+c(23)*d(lcore1(-1))
+c(24)*d(lhicp)+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
The single equation approach -resultsThe single equation approach -results
Short run and long run pass through coefficients
)log(PPI
)1log(CORE
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
The single equation approach –the The single equation approach –the stability of the coefficientsstability of the coefficients
0
0.05
0.1
0.15
0.2
0.25
0.3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
first difference of log PPI
first difference of log CORE1
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
first dif ference of log PPI
f irst dif ference of log CORE1
Short run pass thoughShort run pass though Long run pass thoughLong 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 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 PPI based inflation ranging between 0.22 and 0.25 and with the pass through into CORE1 inflation ranging between 0.11 and 0.07through 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 The long run pass through coefficients are clearly far from stable and seem to be systematically decreasing. Tsystematically decreasing. The pass through into PPI based inflation ranges between he pass through into PPI based inflation ranges between 0.41and 0.24 , while the pass through into CORE1 inflation ranges between 0.33 and 0.41and 0.24 , while the pass through into CORE1 inflation ranges between 0.33 and 0.170.17
The single equation approach –Including the The single equation approach –Including the volatilityvolatility
E views specification
d(lipp)=c(11)+c(12)*deurm+c(13)*d(lipp(-1))+c(14)*d(lhicp)+c(15)*volatilityd(lcore1)=c(21)+c(22)*deurm+c(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 isn’t statistically significant.
Coefficient Std. Error z-Statistic Prob.
C 0.000339 9.71E-05 3.492009 0.0005
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
Garch estimation for the whole sampleGarch estimation for the whole sample
The single equation approach –The single equation approach –Including the volatilityIncluding the volatility
Estimation results
Pass through coefficients
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
)log(PPI
)1log(CORE
Short run pass through Long run pass through
0.21 0.29
0.12 0.24
The single equation approach –testing The single equation approach –testing for appreciation asymmetryfor appreciation asymmetry
E views specification
d(lipp)=c(11)+c(12)*deurm+c(13)*d(lipp(-1))+c(14)*d(lhicp)+c(15)*abs(deurm)d(lcore1)=c(21)+c(22)*deurm+c(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 aren’t statistically significant.
The single equation approach –testing The single equation approach –testing for asymmetryfor asymmetry
E views specification
d(lipp)=c(11)+c(12)*deurm+c(13)*app*deurm+c(14)*d(lipp(-1)) +c(15)*d(lhicp)d(lcore1)=c(21)+c(22)*deurm+c(23)*app*deurm+c(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 deurm>a, 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
The single equation approach –testing The single equation approach –testing for asymmetryfor asymmetry
-2.5000
-2.0000
-1.5000
-1.0000
-0.5000
0.0000
0.5000
1.0000
1.5000
2.0000
2.5000
-0.0367
-0.0267
-0.0167
-0.0067
0.0033
0.0133
0.0233
0.0333
0.0433
0.0533
0.0633
0.0733
c(13)
c(23)
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).
ConclusionsConclusions
• 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%).
• 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
ConclusionsConclusions
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