Exchange Rate Pass-Through and Its Impact on Inflation a Comparative

7/30/2019 Exchange Rate Pass-Through and Its Impact on Inflation a Comparative

1/32

Exchange Rate Pass-Through and its Impact on Inflation: A Comparative

Study for Australia, China and India with Disaggregated Data

Shrabani Sahaa*, and Zhaoyong Zhang

a

aSchool of Accounting, Finance and Economics, Edith Cowan University, 270 Joondalup

Drive, Joondalup, WA-6027, Australia

Abstract

It has been well documented that the exchange rate pass-through to domestic inflation has

decreased significantly in the developed countries. This article analyses the exchange rate

shocks and its pass-through to various level of prices in two emerging economies and

Australia by employing a structural VAR framework over the period 1990-2011. In

particular, we assess the pass-through into import, export, producer and consumer prices in

Australia, China and India in industries including mining, agriculture and manufacturing. We

test whether the exchange rate pass-through to import prices is more complete in any

particular sector and estimate the pass-through to consumer prices to investigate whether

th i li k b t th th h d th i fl ti t th


2/32

there is any linkage between the pass through and the average inflation rate across these

Exchange Rate Pass-Through and its Impact on Inflation: A ComparativeStudy for Australia, China and India with Disaggregated Data

1. INTERDUCTION

Since the middle of the 20

th

century Australias primary trade partners have shifted from

US/Europe to Asia. Natural and mining resources were, and continue to be exploited at

increasing speeds, with economic expansion accelerating in the region. In recent years,

consistent growth in demand from emerging economies most notably China and India, has

driven up demand for Australian resources in the world market, and in turn increases the

demand for the Australian dollar.1

The Australian dollar (AUD) has started floating since

December 1983 and as of August 2012, the AUD is the third most traded currency in the

world. The high demand for Australian dollar pushes it up against all the major currencies

i iddl f 2008 d i D b 2010 i h d i i h U i d S d ll


3/32

An important issue for exchange rate pass-through (ERPT) is the extent to which exchange

rate changes affect the prices of imported and exported goods and the domestic consumer

prices, which is of a major concern for monetary policy. In theory, ERPT refers to the

transmission of changes in exchange rate into import (export) prices of specific goods in the

destination market currency. The pass-through effects of exchange rate changes on import

prices will contribute to the domestic inflation, while on the export prices will affect the price

competitiveness, hence net exports and real activity. ERPT is said to be incomplete if the

import (export) prices change by less than one. Whether ERPT is incomplete or pervasive, it

is expected that an appreciation of the currency reduces import prices and the reverse ensues

in case of depreciation (Tivig, 1996; Gagonon and Knetter, 1995; Varangis and Duncun,

1993; Krugman, 1987).

Since the 1970s, there has been a huge number of studies to investigate the reasons why the

degree of e change rate pass thro gh is not eq al to nit e en in the long r n and h the


4/32


5/32

import price pass-through reflects the price behaviour of foreign firms and this behaviour

may not be strongly related to the home inflationary environment. Thus evidence on the pass-

through to domestic prices (e.g., consumer price index (CPI)) would provide a more

appropriate test of the Taylor view. Gagonon and Ihrig (2001) explore the relationship

between CPI pass-through and inflation stabilisation for eleven industrial countries but they

do not find a systematic relation between the pass-through and the monetary behaviour. On

the other hand, Nogueira and Le_on-Ledesma [2008] and Shintani et al. (2009) test the

hypothesis in the context of nonlinear time-series models and find that inflation appears to

drive smooth changes in ERPT regimes. These studies, however, focus on specific nonlinear

functional forms and are thus more restrictive.

This research presents a comparative study by exploring the literature relating pass-through

for import, export as well as domestic prices in relatively small economy like Australia and

t l l i i Chi d I di f th i d 1990 2011 b l i th


6/32

China and India and whether these countries require changing the monetary policy targets.

Such a study will undoubtedly contribute to the available vast literature on ERPT

relationship, and more importantly, to the debate between the US and China with regard to

Chinese trade surplus against the US even when its currency is appreciating.

The methodology used in this study is vector autoregressive (VAR) techniques, in which

time-series behavior of the bi-lateral exchange rate and a set of prices are examined to

assessing the responses of import, export, producer and consumer prices to exchange rate

shock with a base line model. Specifically, the empirical analysis investigates the exchange

rate pass-through in a set of prices along the distribution chain to assess producers business

strategy. Second, the impulse-response functions (IRFs) from the VAR estimation are used to

calibrate the key behavioural parameters that can help reproduce the pattern of pass-through

and external adjustment in these three countries.


7/32

2. ANALYTICAL FRAMEWORK

The main focus of the analysis is to estimate the exchange rate pass-through for the prices of

import and export in Australia, China and India using bilateral trade indexes. Along with

import and export prices the main variables under consideration are producer prices and

consumer prices. We first examine the pass-through of exchange rate and import price

fluctuations to domestic producer and consumer prices across three countries using a standard

VAR model specified in equation (1):

tktkttt XXXX +++++= ......2211 (1)

where Xt denotes vector of endogenous variables, t is a vector of innovations that may be

contemporaneously correlated but are uncorrelated with their own lagged values and

uncorrelated with all right-hand side variables, is a vector of constants and are matrices


8/32

=

cpi

t

exr

t

impi

t

ip

t

inrt

t

oip

t

cpi

t

exr

t

impi

t

ip

t

inrtt

oip

t

SSSSSS

SSSSS

SSSS

SSS

SS

S

666564636261

5554535251

44434241

333231

2221

11

0

00

000

0000

00000

(2)

Variables ordered in the base model are to examine the identified shocks contemporaneously

affect their corresponding variables and those variables that are ordered at a later stage, but

have no impact on those that are ordered before. Oil price inflation and industrial output

reflect real sector of the economy whereas interest rate is included to examine the impact of

monetary policy. Oil price shock is ordered first because the reduced-form residuals of oil

prices are unlikely affected contemporaneously by any other shocks except oil price shock

itself, while it may affect the reduced form residuals of all equations and thus all variables in


9/32

literature, we place the domestic prices at the bottom of the VAR ordering with the

assumption that the price variable is contemporaneously affected by all other shocks while

the price shock has no contemporaneous impact on the other variables (see Hahn [2003]).

Since exchange rate and domestic prices variables are the main focus of the analysis, we

employ and order different price variables in the VAR model according to the distribution

chain to assess the pass-through effect of the exchange rate change in the empirical analysis.

In the second step, we repeat the same procedure for three different sectors i.e., mining,

agriculture and manufacturing. Finally, we replace the export price in place of import price to

examine the pass-through effect of exchange rate.

3. DATAWe use in this study the unit values of bilateral exports and imports between the concerned

economies as proxies for the bilateral import and export prices of the three ecoonmies. All


10/32

the HEGY tests for unit roots at seasonal frequencies. Table 1 reports the results for the

standard unit root tests. We select the lag length following Akaike Information Criteria

(AIC). We also report the results with the first-differenced series to confirm that all the

variables under investigation are I(1). Regression equation for unit root test includes both

intercept and trend. From Table 1, we can infer that except CPI for Australia and PPI for

India all variables in levels are non-stationary. The HEGY seasonal unit root tests confirm

these results and further indicate that we can reject unit roots at the 5% level at all the

seasonal frequencies with the exception of zero frequency (Table 2). Given these properties

of the data, VAR model in the first differences of the non-stationary variables considers as an

appropriate specification of the models.

[Insert Tables 1 and 2 about here]

4. RESULTS


11/32

Impulse response

In this subsection we estimate the VAR model specified previously and examine the degree

of pass-through from the exchange rate shock to the three price variables, namely, import

price (for three different sectors i.e. imp1, imp2 and imp3), producer price index (ppi) and

consumer price index (cpi) in each economy at bi-lateral level, i.e. Australia-China, India-

Australia and China-India.3

The lag order of the VAR model is selected based on the Akaike

information criterion (AIC).We first estimate the baseline models, and then analyse the

impulse response functions of a variable in response to the shock over a period of 20 months.

As the bi-lateral exchange rate is used for each bi-lateral trade countries and is defined

indirectly as number of units of the second currency equivalent to the one unit of the first

currency,4

an increase in the exchange rate implies an appreciation of the first currency and

depreciation of the second country concerned. Figure 2 plots the exchange rate shocks and its

i t th i bl ti t d b i i l t i ti th t t l VAR


12/32

large negative effects in the initial months can be seen for manufacturing sector in India and

for mining and natural resources sector in China. For China, agriculture sector does not show

a strong response to the exchange rate shocks. On the other hand, CPI shows a large positive

response in Australia and the response increases over time, which indicates that the exchange

rate does matter for domestic inflation in Australia. In other words, an increase in Australian

dollar decreases the import price but increases the domestic prices, which is quite conflicting.

In contrast, the response of CPI is quite small in India, CPI increases initially in response to

an increase in Indian rupee, however, the effect dies out after 10 months. The result indicates

that exchange rate variation does not cause domestic price variation. In China, the impact on

CPI is nil in the initial two months and after that the CPI decreases and the negative response

increases over time.

In Australia, PPI shows a large negative response in the first instance, but the effect dies out

l l ti H f Chi PPI i t bl i th i iti l t th d th


13/32


14/32

and is also industry based, a reflection of different business strategie across sectors. We also

find evidence that exchange rate shock contributes to domestic inflation.

4.2. Export price pass-throughFigure 1 shows the impulse response for the export prices as well in all three economies.

Figure 1(a) shows that export price for mining and natural resources decreases initially after

two months it has positive effect suggesting that exchange rate shock increases the export

price for the mining product. However, the export price for agriculture and manufacturing

sector initially have positive effect but the effects become negative over time. On the other

hand, in India, all three sectors show positive effect in the beginning but it became negative

over time. In China manufacturing sector shows large positive effect after 5 months and the

positive effect increases over time. However, mining and agriculture sectors responses are

negative throughout. Overall, the greater impact in terms of export price is in Australia


15/32

import prices, exchange rate and domestic price shocks are the next important factor in

explaining import price variance in Australia for mining and natural resources, where the

share changes from 0.74% to 1.33% for the former and from 0.54% to1,52% for the latter

(Table 4). In india, the domestic prices and production are the next important factor in

accounting for the variance of IMP, in addition to its own shocks. The exchange rate shocks

account for about 3.05% of import price variance in the manufacturing in India. It is

interesting to note that in China, the variance of import prices is largely explained by the IMP

shock originated from the mining and energy sector, which accounts for around 50% of the

variance in all the three industries. This finding is consistent with our early discussion of

Chinas high dependency on the imported mining and energy products from the world. The

impact of the exchange rate shocks on the import price is not strong.

[Insert Table 4 about here]


16/32


17/32

given on revisiting the monetary policy target and how it can be restructured to control

inflation.

References

[1] Malin Adolfson, Incomplete exchange rate pass-through and simple monetary policy

rules, Journal of International Money and Finance, Elsevier, vol. 26(3) (2007), 468494.

J. Bailliu, E. Fujii, Exchange rate pass-through and the inflation environment in industrialized

countries: an empirical investigation, Bank of Canada, Working Paper No. 21 (2004).

[2] H. Bouakez, N. Rebei, Has exchange rate pass-through really declined in Canada? Journal

of International Economics 75 (2008), 249-267.

[3] J C L G ldb E h h h i i i Th R i f


18/32

[10] J. Gagnon, J. Ihrig, Monetary policy and exchange rate pass-through, Board of

Governors of the Federal Reserve System International Finance Discussion Paper No.704

(2001).

[11] J.E. Gagnon, J. Ihrig, Monetary policy and exchange rate pass-through, International

Journal of Finance and Economics 9 (2004), 315338.

[12] P. Goldberger, M. Knetter, Goods Prices and Exchange Rates: What Have We Learned?

Journal of Economic Literature 35 (1997), 1243-1272.

[13] E. Hahn, Pass-through of external shocks to euro area inflation, European Central Bank

Working Paper No. 243 (2003).

[14] S. Hylleberg, R.F. Engle, C.W.J. Granger, B.S. Yoo, Seasonal Integration and

Cointegration, Journal of Econometrics 44 (1990), 215-238.

[15] T. Ito, K. Sato, Exchange rate changes and inflation in post-crisis Asian economies:

vector autoregression analysis of the exchange rate pass-through, Journal of Money, Credit

and Banking 40 (7) (2008), 1047-1438.

[16] P. Krugman, Pricing to market when the exchange rate changes, In: S. Arndt, J.

Richardson (Eds.), Real-financial linkages among open economies, Cambridge: MIT Press

(1987).

[17] J M C h P h h f h d i i d i i fl i i


19/32


20/32

Tables:

Table 1: Unit root test

Panel A: Augmented Dickey Fuller

Variable Country

Australia India China

Lag Test-Stat Lag Test-Stat Lag Test-Stat

Import Price

Mining and natural

resources

0 -16.04960 1 -7.761384 12 -5.064246

Import Price

Mining and natural

resources

6 -11.68837 7 -10.41100 11 -7.89595

Import Price

Agriculture and

processed product

3 -4.550816 2 -3.885534 0 -3.038236

Import Price

Agriculture and

processed product

3 -

13.32613

2 -14.61790 0 -13.76888

Import Price

Manufacturing

2 -5.711333 0 -13.81545 13 -6.421005

Import Price

Manufacturing

2 -16.33785 6 -11.40391 12 -5.012445

Export Price

Mining and natural

resources

0 -15.38773 0 -7.023626 3 -3.022366


21/32

PANEL B: Phillips-Perron test statistic

Australia India China

Bandwidth Test-Stat Bandwidth Test-Stat Bandwidth Test-StatImport Price

Mining and natural

resources

1 -16.04956 5 -13.95957 4 -3.692995

Import Price

Mining and natural

resources

43 -105.3472 56 -98.28894 2 -16.12462

Import Price

Agriculture and

processed product

9 -13.26806 10 -11.82276 4 -3.174099

Import Price

Agriculture and

processed product

25 -63.57073 33 -71.30065 1 -13.76832

Import Price

Manufacturing

10 -12.14113 2 -13.81717 6 -3.236754

Import PriceManufacturing

10 -34.19651 39 -73.46150 5 -8.398834

Export Price

Mining and naturalresources

6 -15.53488 5 -7.022370 10 -10.04325

Export Price

Mining and natural

resources

51 -85.95189 22 -30.33449 28 -62.85980

Export PriceAgriculture and

processed product

4 -16.71577 4 -3.514836 7 -13.46409

Export Price

A i l d

51 -124.2351 0 -15.51379 71 -130.5654


22/32

Table 2: HEGY test results

Australia F-Statistics (p-value)

Variables 1 2 3=4 5=6 7=8 9=10 11=12

Import PriceMining and

natural

resources

-2.011 -3.521

11.37(0)

7.639(0.001) 5.813(0.004) 14.141(0) 13.377(0)

Import Price

Agriculture

-2.099 -2.62 11.825(0) 8.142(0) 12.897(0) 13.909(0) 11.654(0)

Import Price

Manufacturingand processed

product

-3.517 -1.927

8.138(0)

1.627(0.199) 10.016(0) 7.659(0.001) 5.036(0.007)

CPI 4.714 -3.928 11.005(0) 8.956(0) 11.94(0) 10.295(0) 18.551(0)

Oilprice -4.515 -3.186 13.302(0) 9.771(0) 13.71(0) 5.274(0.006) 17.019(0)

PPI -1.793 -3.227 9.808(0) 9.815(0) 9.3(0) 9.882(0) 7.537(0.001)

Interest 4.851 -4.349 26.06(0) 10.075(0) 15.398(0) 16.189(0) 23.65(0)

Aus-Chn ER 2.977 -3.646 16.03(0) 15.11(0) 8.092(0) 13.029(0) 16.1(0)


23/32

China F-Statistics (p-value)

Variables 1 2 3=4 5=6 7=8 9=10 11=12

Import PriceMining and

natural

resources

-4.734 -2.775 10.339(0) 11.071(0) 12.564(0) 9.789(0) 12.31(0)

Import Price

Agriculture-0.967 -2.915 7.905(0.001) 11.951(0) 23.06(0) 7.077(0.001) 43.077(0)

Import Price

Manufacturingand processed

product

-3.343 -3.559 10.494(0) 6.291(0.003) 18.778(0) 16.976(0) 11.279(0)

CPI 4.806 -4.058 22.52(0) 20.071(0) 13.72(0) 20.912(0) 29.084(0)

Oilprice -4.515 -3.186 13.302(0) 9.771(0) 13.71(0) 5.274(0.006) 17.019(0)

PPI 4.929 -2.815 18.618(0) 11.71(0) 16.69(0) 7.097(0.001) 28.748(0)

Interest 4.035 -3.626 12.124(0) 11.272(0) 11.364(0) 11.722(0) 21.191(0)

Chn-Ind ER 2.848 -3.583 9.388(0) 16.341(0) 7.988(0.001) 17.467(0) 18.524(0)

Table 3: Dynamic ERPT


24/32


25/32

10 2.658172 1.017925 1.520355 1.928274 0.783499 6.02105 87.6668 0.326941 0.220207 0.514944

15 2.84514 1.261467 2.180011 2.225719 0.923696 5.495568 86.63114 0.478658 0.275276 0.528468

20 2.942401 1.327961 3.004125 2.36853 1.042964 5.16063 85.62298 0.650287 0.290745 0.531778

IMP1

1 0.738975 0.739162 0.548231 0.057526 0.291481 0.157286 0.152149 98.05417 0 0

5 0.770622 0.967807 1.456352 0.396854 0.398371 0.197106 0.181853 92.20339 3.862608 0.335655

10 0.772265 1.167883 1.490963 0.43029 0.413554 0.198405 0.284462 91.82686 3.852765 0.334814

15 0.77336 1.27915 1.506313 0.440021 0.420192 0.203614 0.402379 91.57041 3.843016 0.334906

20 0.77417 1.334162 1.520632 0.448137 0.425653 0.210607 0.508302 91.38149 3.835792 0.335223

IMP2

1 0.097255 0.364782 0.01278 0.330572 2.206091 0.286146 2.89E-05 0.000557 96.79904 0

5 0.104685 0.446387 1.224204 1.632931 2.8569 0.64933 2.100942 0.565505 90.32817 0.195636

10 0.106082 0.589311 1.358557 1.666069 2.783567 0.644664 4.000107 0.620917 88.13537 0.201439

15 0.106794 0.820002 1.397684 1.656532 2.746558 0.675241 4.896622 0.621026 86.98422 0.202115

20 0.107255 1.092803 1.421416 1.646371 2.723126 0.727842 5.318205 0.619948 86.24873 0.201558

IMP3

1 49.28681 0.002834 0.065817 0.011833 0.026873 0.599498 0.00074 0.725663 0.062309 98.50443

5 53.30216 0.010322 0.074835 0.022433 0.143079 1.112736 1.700278 2.07468 1.170084 93.6915510 53.63906 0.090157 0.110083 0.035705 0.141358 1.26141 2.626987 2.051198 1.159073 92.52403

15 53.78662 0.156077 0.167845 0.04134 0.141763 1.282899 2.986022 2.047156 1.157977 92.01892

20 53.84859 0.186127 0.22926 0.041595 0.145072 1.281153 3.100301 2.050845 1.158084 91.80756


26/32

(b) Variance Decomposition: India-Australia

Period S.E. AUS_IND_ER CPI PPI OILPRICE INTEREST IP IMP1 IMP2 IMP3

AUS_

IND_

ER 1 0.777641 100 0 0 0 0 0 0 0 0

5 1.859316 93.05037 0.233127 0.274952 0.027334 0.145299 0.09979 0.048539 0.192251 5.928344

10 2.249318 89.05653 1.348578 0.334651 0.036582 0.830914 0.810055 0.038808 0.778374 6.765507

15 2.41882 84.93563 2.905766 0.315638 0.069682 1.870687 1.995415 0.075198 1.283779 6.5482

20 2.533875 80.64878 4.731952 0.29009 0.118583 2.959321 3.240603 0.160504 1.670141 6.180026

CPI

1 0.719386 0.477691 99.52231 0 0 0 0 0 0 0

5 2.102108 4.681299 83.00505 5.508517 3.205997 0.139325 0.482316 2.24535 0.280125 0.452021

10 3.111335 4.89079 79.82528 4.972166 3.688138 0.310598 2.003924 2.909553 1.058669 0.340883

15 3.938069 4.615366 77.75212 4.290959 3.626635 0.887001 3.758069 3.067105 1.701312 0.301431

20 4.692612 4.501443 75.75082 3.718621 3.496135 1.633937 5.317993 3.132588 2.182962 0.265501

PPI

1 0.555343 2.781673 14.28635 82.93198 0 0 0 0 0 0

5 1.525606 21.75313 6.34841 63.05217 1.66058 0.001512 6.145895 0.397408 0.11464 0.526254

10 2.191266 29.83529 5.949516 45.11977 1.465641 0.001403 14.93496 0.209338 0.616807 1.86727915 2.734883 32.23351 7.745747 34.09661 1.314493 0.001688 20.61618 0.16792 1.395245 2.428598

20 3.215134 32.15658 10.96705 27.13415 1.286865 0.016497 23.69393 0.130284 2.102139 2.512511

OILPRICE

1 0.094699 2.949721 0.697094 6.496209 89.85698 0 0 0 0 0

5 0.101498 9.430287 1.590146 6.764835 79.23827 0.366634 0.313541 0.538405 0.369071 1.38881

10 0.101754 9.390361 1.612566 6.905187 78.84927 0.487655 0.361359 0.608388 0.392505 1.392704

15 0.101855 9.39024 1.675372 6.930251 78.69312 0.548432 0.362702 0.612195 0.395769 1.391924

20 0.101946 9.412738 1.752451 6.925117 78.55389 0.577167 0.373755 0.61155 0.395231 1.398098

INTEREST

1 0.251624 3.245433 0.183421 5.167349 0.050994 91.3528 0 0 0 0

5 0.585138 5.58427 3.394412 4.464401 0.029001 80.8831 0.559271 4.707503 0.376247 0.001797

10 0.7934 5.753938 2.724655 4.885021 0.01999 78.8966 1.148717 5.619617 0.926104 0.025363

15 0.920998 5.75428 2.146924 5.384126 0.034703 78.26785 1.620971 5.532231 1.202088 0.056829

20 1.006032 5.75643 1.808843 5.827326 0.064999 77.87442 1.926345 5.315919 1.332095 0.09362

IP1 4.224124 0.180851 0.17789 0.23096 0.416289 0.284599 98.70941 0 0 0

5 5.854301 0.290356 1.055447 3.984933 0.918051 2.198169 86.98018 0.800909 2.654323 1.117636


27/32

10 6.623948 1.29592 3.936475 3.547341 0.738794 2.470092 82.02833 0.811567 4.0194 1.152082

15 7.144408 2.435106 8.511982 3.060857 0.752124 2.660194 76.18398 0.711871 4.600378 1.083503

20 7.626587 3.311019 13.66013 2.734044 0.862676 2.888233 70.02306 0.727942 4.818743 0.974153

IMP1

1 0.367338 0.068707 0.753724 0.59847 0.016252 0.371781 0.008611 98.18246 0 0

5 0.384906 0.437494 1.78589 0.974785 0.248071 0.438454 1.988167 92.92314 1.140287 0.063708

10 0.390065 0.576024 2.750527 0.950623 0.288756 0.48722 3.090863 90.64119 1.122838 0.091964

15 0.39259 0.575892 3.427648 0.938852 0.320558 0.483589 3.427965 89.58444 1.111385 0.129667

20 0.39426 0.607117 3.907034 0.931883 0.34083 0.500172 3.527208 88.90999 1.102163 0.173602

IMP2

1 0.252124 0.164227 0.109404 0.013724 0.274516 0.001447 1.953085 0.575035 96.90856 0

5 0.291417 0.331814 0.53543 0.383203 1.419663 0.375833 8.505863 2.50259 85.88939 0.056215

10 0.305672 0.505165 1.752946 0.60887 1.49182 0.356081 12.65828 3.164927 79.31145 0.15046

15 0.310599 0.497153 2.49383 0.642126 1.521959 0.436137 13.76958 3.475056 76.92613 0.238033

20 0.312631 0.509679 2.851718 0.636592 1.529014 0.59907 14.00096 3.63234 75.94395 0.296678

IMP3

1 1.474153 1.662288 0.352212 1.479343 1.072694 0.079739 0.598833 0.000101 0.016467 94.73832

5 1.514275 2.882119 0.668426 1.767819 2.007341 0.155225 1.989476 0.289974 0.049554 90.1900710 1.52718 2.923842 1.30597 1.935345 1.985425 0.176732 2.603566 0.333689 0.049935 88.6855

15 1.53573 2.943962 1.775504 1.991616 1.977229 0.181975 2.927888 0.404838 0.050133 87.74686

20 1.542485 3.052067 2.072826 2.017088 1.969398 0.212541 3.109442 0.474603 0.050833 87.0412


28/32

(c) Variance Decomposition China-India

Period S.E. CHI_IND_ER CPI PPI OILPRICE INTEREST IP IMP1 IMP2 IMP3

CHI_IND_

ER 1 0.08705 100 0 0 0 0 0 0 0 0

5 0.250714 95.07755 0.174727 0.108305 0.381794 1.241276 1.473489 0.504945 0.924393 0.11351910 0.35964 89.87563 1.454349 0.215864 0.642835 2.248428 2.048582 1.859983 0.949419 0.704905

15 0.43079 85.23697 3.172668 0.200641 0.735552 3.356958 1.897137 3.14238 1.052859 1.204834

20 0.480664 81.72673 4.688667 0.169658 0.733543 4.570874 1.630215 3.881897 1.161855 1.436561

CPI

1 0.556232 1.326635 98.67336 0 0 0 0 0 0 0

5 1.403689 3.574128 64.71282 0.758432 0.486362 0.294716 28.24898 0.732275 0.052135 1.14015

10 2.009839 6.974141 49.56525 0.460427 0.43865 0.163185 40.83848 0.650464 0.138934 0.770472

15 2.36658 8.535111 41.84716 0.420742 0.344702 0.144896 46.47828 1.153966 0.115183 0.959964

20 2.576486 9.337367 37.2568 0.364278 0.311406 0.201845 48.84954 2.065041 0.107663 1.506057

PPI

1 0.595779 0.20589 1.814317 97.97979 0 0 0 0 0 0

5 2.439568 1.068511 10.19119 65.48355 11.26889 0.372513 7.483837 3.682795 0.27049 0.178221

10 3.401099 4.922133 16.18012 43.94836 9.984905 1.402952 18.83603 3.567484 0.850426 0.307582

15 3.788405 7.788454 16.25298 35.56106 8.15474 2.250716 24.64573 3.566671 0.880644 0.898999

20 3.986615 9.110768 14.92305 32.1722 7.524643 2.753919 26.0312 4.697733 0.876156 1.910333

OILPRICE

1 0.09355 2.443477 3.881506 0.50662 93.1684 0 0 0 0 0

5 0.103 3.696073 3.701944 1.019021 77.91292 0.195308 4.633398 1.636528 6.456023 0.748789

10 0.104412 3.781433 3.65869 2.586236 76.26948 0.217052 4.535177 1.832799 6.297029 0.822101

15 0.104772 3.770631 3.803387 2.700546 75.85625 0.216035 4.548961 1.96937 6.259924 0.874897

20 0.104868 3.769004 3.87169 2.728523 75.72093 0.216398 4.600169 1.969191 6.249409 0.874688

INTEREST 1 0.23069 0.125794 1.044376 0.130412 0.894954 97.80446 0 0 0 0

5 0.462372 4.740466 1.627085 0.217729 0.452614 90.74008 1.036873 0.857621 0.043822 0.283708

10 0.63924 8.117678 1.461055 0.274395 0.372626 81.89359 6.518095 0.806265 0.028295 0.528006

15 0.783121 9.848646 2.363362 0.898649 0.264403 71.93558 13.00067 1.234351 0.050692 0.40365

20 0.903939 10.63528 3.240105 1.159976 0.228185 63.72311 18.77272 1.851866 0.074507 0.314251


29/32

IP

1 2.922172 0.01207 2.75279 7.993034 0.342499 0.275792 88.62381 0 0 0

5 3.522173 4.570423 2.86768 7.808768 3.478111 0.299036 78.71811 0.348143 1.091616 0.818114

10 3.777436 5.533947 3.347109 6.825491 3.081885 0.55213 76.49672 1.471175 0.990586 1.700953

15 3.899299 5.705873 3.215848 6.425145 2.944365 0.80504 74.59776 2.756983 1.006234 2.542753

20 3.947954 5.8037 3.1445 6.372606 2.873773 1.04191 73.55648 3.226039 1.062207 2.918784

IMP1

1 0.515482 0.67666 0.18555 0.275104 2.258129 0.084236 0.162473 96.35785 0 0

5 1.068372 0.400534 0.295954 0.718663 1.316867 0.096217 5.778285 57.95618 7.719821 25.71748

10 1.297988 0.365899 1.150112 2.602152 0.975957 0.133084 9.076307 53.06804 8.257845 24.3706

15 1.342779 0.345389 1.650374 3.169756 0.982067 0.148134 9.451431 51.77484 8.472295 24.00572

20 1.354373 0.353382 2.035741 3.16041 0.967157 0.160922 9.335705 51.52947 8.523547 23.93367

IMP2

1 0.251291 0.021867 0.001458 0.351023 1.168537 0.017583 0.293397 73.21731 24.92882 0

5 0.527545 0.365789 0.261955 1.147563 0.948302 0.016347 3.692058 51.06528 17.21541 25.28729

10 0.640532 0.290565 1.503832 2.737165 0.708815 0.069365 6.358041 48.73098 15.34904 24.2522

15 0.662985 0.278179 2.399727 3.176297 0.702371 0.106693 6.477236 47.79311 15.16224 23.90415

20 0.669779 0.273432 3.07946 3.148278 0.689973 0.148713 6.377694 47.46809 15.07037 23.744

IMP3

1 3.424496 0.172598 0.01159 0.224417 1.473111 0.008866 1.249983 60.35996 6.127153 30.37232

5 11.35091 0.489402 0.360877 0.463983 1.491026 0.060919 7.649137 46.13791 8.304558 35.04219

10 13.97404 0.444266 1.256461 2.923921 1.09834 0.120371 10.71094 44.65607 8.42276 30.36687

15 14.47395 0.421474 1.67021 3.630527 1.130294 0.131472 11.1089 43.74271 8.63616 29.52826

20 14.59925 0.433064 1.99987 3.614806 1.113703 0.138794 10.98655 43.64688 8.691891 29.37445


30/32


31/32

(b) Impulse response for India-Australia

-.08

-.06

-.04

-.02

.00

.02

.04

2 4 6 8 10 12 14 16 18 20

Response of IMP1 to CholeskyOne S.D. AUS_IND_ER Innovation

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20


-.3

-.2

-.1

.0

.1

.2

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

.04

.05

2 4 6 8 10 12 14 16 18 20

Response of EX1 to CholeskyOne S.D. AUS_IND_ER Innovation

-.08

-.06

-.04

-.02

.00

.02

.04

.06

2 4 6 8 10 12 14 16 18 20


-6

-5

-4

-3

-2

-1

0

1

2

3

2 4 6 8 10 12 14 16 18 20


-.1

.0

.1

.2

.3

.4

.5

.6

.7

2 4 6 8 10 12 14 16 18 20

Response of WPI to CholeskyOne S.D. AUS_IND_ER Innovation

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12 14 16 18 20

Response of CPI to CholeskyOne S.D. AUS_IND_ER Innovation

(c) Impulse response for China-India


32/32

Exchange Rate Pass-Through and Its Impact on Inflation a Comparative

Documents

Transcript of Exchange Rate Pass-Through and Its Impact on Inflation a Comparative