exchange rate pass-through and its impact on inflation a comparative
TRANSCRIPT
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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
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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
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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
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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
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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.
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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
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=
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
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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
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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
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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
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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
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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
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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]
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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
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[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
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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
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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
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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)
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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
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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
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(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
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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
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(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
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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
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(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
Response of IMP2 to CholeskyOne S.D. AUS_IND_ER Innovation
-.3
-.2
-.1
.0
.1
.2
2 4 6 8 10 12 14 16 18 20
Response of IMP3 to CholeskyOne S.D. AUS_IND_ER Innovation
-.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
Response of EX2 to CholeskyOne S.D. AUS_IND_ER Innovation
-6
-5
-4
-3
-2
-1
0
1
2
3
2 4 6 8 10 12 14 16 18 20
Response of EX3 to CholeskyOne S.D. AUS_IND_ER Innovation
-.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
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