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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