YU_Pass-through_of_exchange_rate_shocks

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SCHOOL OF ECONOMICS
HONOURS THESIS
Pass-Through of Exchange Rate Shocks to
Inflation in an Australian Context
Author: Jason Yu
Supervisor: A/Prof. Glenn Otto
Submitted as part of the requirement for the degree of:
B. Commerce (Finance and Financial Economics)/B. Economics
(Economics)
Honours in Economics
22nd
October, 2012

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DECLARATION
I hereby declare that this thesis is my own original work, and to the best of my
knowledge, it contains no material that has been published or written by other author(s)
except where due contributions been acknowledged. This thesis has not been
submitted to any other university or institutions as part of the requirements for another
degree or other award.
Jason Yu
22 October 2012

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ACKNOWLEDGEMENTS
In accomplishment of this thesis, I cannot help to think that my achievements were the
fruits of many others' support and encouragement. First and foremost, I would like to
sincerely thank my supervisor Associate Professor Glenn Otto for his tremendous
support and guidance throughout my Honours year. I am extremely grateful for the
time and effort spent to help me accomplish my thesis. Without the guidance of my
supervisor, the construction of this thesis would have been impossible.
I am also indebted to James Morley for his valuable comments and discussions both in
and outside the seminars. I wish to thank Andy Tremayne, Mariano Kulish, Nigel
Stapledon, and Scott French for their helpful comments and advice during the final
seminar. Additionally, I would like to thank the Donors of Australian School of
Business at UNSW for the Honours scholarship, which provided me financial support
throughout the Honours year.
To the Honours cohort 2012: Thank you for all those fun and stressful times spent
together. Also, I wish to thank Josef Manalo for his mentorship in preparation for my
Honours year; Jahanzeb Khan and Anthony Chan for proof-reading my whole thesis;
and Anthony Chan's family for the affordable accommodation. Finally, I would like to
thank Hong Il Yoo for his expertise in microeconometrics.
Last, but certainly not least, thanks goes to my family who continually supported me
both in physical and spiritual ways that provided me strong motive to plough through
the Honours year.

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TABLE OF CONTENTS
Abstract.......................................................................................................................11
1. Introduction............................................................................................................12
2. Literature Review ..................................................................................................16
2.1 Australian Pass-through Literature......................................................................16
2.2 Partial Equilibrium Approach .............................................................................18
2.3 General Equilibrium Approach ...........................................................................20
2.4 Empirical-based Literature..................................................................................22
3. Theoretical Model ..................................................................................................25
4. Econometric Methodology ....................................................................................28
4.1 Structural Vector Error Correction Model ..........................................................28
4.2 Identification Under Weak Exogeneity...............................................................30
5. First Stage Pass-through: Exchange Rate to Import Price................................33
5.1 Data Description and Properties..........................................................................33
5.2 Model Estimation and Identification...................................................................37
5.3 Main Results for First Stage Pass-through..........................................................40
5.4 Australian Inflation Targeting Subsample ..........................................................43
5.5 Rolling Window for Coefficient Stability...........................................................48
6. Second Stage Pass-through: Import Price to Inflation.......................................49
6.1 Data Description and Properties..........................................................................49
6.3 Main Results for Second Stage Pass-through .....................................................54
6.5 Australian Inflation Targeting Subsample ..........................................................58
6.6 Rolling Window for Coefficients Stability .........................................................64
7. Combined Stage Pass-through: Exchange Rate to Retail Price ........................65

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7.1 Data Properties ....................................................................................................65
7.3 Main Results for Combined Stage Pass-through.................................................69
7.4 Australian Inflation Target Subsample ...............................................................73
7.6 Rolling Window for Coefficients Stability .........................................................74
8. Conclusion and Limitations ..................................................................................75
8.1 Conclusion...........................................................................................................75
8.2 Limitations and Future Research.........................................................................76
Appendix 1: Data Construction................................................................................79
A1.1 Retail Prices of Imported Consumption Goods................................................79
A1.2 Prices of Consumption Imports Over-the-docks..............................................81
A1.3 World Export Price for Consumption Goods...................................................81
A1.4 Nominal Effective Exchange Rate ...................................................................85
A1.4 Costs Borne By Importers and Retailers ..........................................................85
Appendix 2: Reduced-form Level VAR Lag Selection...........................................87
Appendix 3: Further Test for Cointegration...........................................................91
Appendix 4: Approximate 2 Standard Error Bands Based on Levels VAR ........92
Appendix 5: Data Properties and Model Estimation for Subsamples ..................98
Appendix 6: Impulse Response Functions for Further Robustness Tests..........102
Appendix 7: Rolling Window for Coefficient Stability ........................................104
Bibliography.............................................................................................................105

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LIST OF TABLES
Chapter 5
Table 5.1: First Stage Augmented Dickey-Fuller Test Results ....................................36
Table 5.2: First Stage Pass-through Engle-Granger Test Results.................................36
Table 5.3: First Stage Pass-through Johansen Cointegration Test Results...................36
Table 5.4: First Stage Pass-through Estimation Results...............................................36
Table 5.5: First Stage Adjustment Coefficients Implied By Johansen Normalised
Coefficients on Error Correction of VECM(2).............................................................38
Table 5.6: First Stage Variance Decomposition of Permanent and Transitory Shocks
for Full Sample Period..................................................................................................42
for Subsample 1983Q2-1993Q1 ...................................................................................46
for Subsample 1993Q2-2010Q1 ...................................................................................47
Chapter 6
Table 6.1: Second Stage Augmented Dickey-Fuller Test Results................................52
Table 6.2: Second Stage Pass-through Engle-Granger Test Results ............................52
Table 6.3: Second Stage Pass-through Johansen Cointegration Test Results ..............52
Table 6.4: Second Stage Pass-through Estimation Results...........................................52
Table 6.5: Adjustment Coefficients on Error Correction of VECM(2)........................53
Table 6.6: Variance Decomposition of Permanent and Transitory Shocks: Full Sample
Period............................................................................................................................57
Table 6.7: Second Stage Variance Decomposition of Permanent and Transitory Shocks
Implied By DOLS Estimates: 1983Q2-1993Q1 ...........................................................61

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LIST OF TABLES (CONT.)
Chapter 6 (Cont.)
Table 6.8: Second Stage Variance Decomposition of Permanent and Transitory Shocks
Implied By DOLS Estimates: 1993Q2-2010Q1 ...........................................................63
Chapter 7
Table 7.1: Combined Stage Pass-through Estimation Results......................................65
Table 7.2: Combined Stage Pass-through Four-Variables VECM Engle-Granger Test
Results...........................................................................................................................66
Table 7.3: Combined Stage Pass-through Four-Variables VECM Johansen
Cointegration Test Results............................................................................................66
Table 7.4: Combined Stage Pass-through Four-Variables VECM Estimation Results 66
Table 7.5: Adjustment Coefficients on Error Correction of VECM(2)........................68
Table 7.6: Combined Stage Variance Decomposition of Permanent and Transitory
Shocks: Full Sample Period..........................................................................................72
Table 7.7: Combined Stage Pass-through Subsamples Estimation Results..................73
Appendices
Table A1.1: Australia's Major Trading Partner TWI Weights......................................82
Table A1.2: Cost Index Weights...................................................................................87
Table A2.1: Level VAR Lag Length Selection Criterions ...........................................89
Table A2.2: Level VAR Residual Serial Correlation LM Test ....................................90
Table A3.1: Significance of Error Correction Term Test.............................................91
Table A5.1: First Stage Pass-through Estimation Results: 1983Q2-1993Q1...............99
Table A5.2: First Stage Pass-through Adjustment Coefficients on Error Correction of
VECM(2) Implied By DOLS Estimates: 1983Q2 - 1993Q1........................................99

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LIST OF TABLES (CONT.)
Appendices (Cont.)
Table A5.3: First Stage Pass-through Estimation Results: 1993Q2 - 2010Q1.............99
Table A5.4: Second Stage Pass-through Estimation Results: 1983Q2 - 1993Q1.......100
Table A5.5: Second Stage Pass-through Adjustment Coefficients on Error Correction
of VECM(2) Implied By DOLS Estimates: 1983Q2 - 1993Q1..................................100
of VECM(2) Implied By Johansen Normalised Coefficients: 1983Q2 - 1993Q1......100
Table A5.7: Second Stage Pass-through Estimation Results: 1993Q2 - 2010Q1.......101
of VECM(2) Implied By DOLS Estimates: 1993Q2 - 2010Q1..................................101

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LIST OF FIGURES
Chapter 1
Figure 1.1: Plot of Exchange Rate, Import Price, and Retail Price ..............................12
Chapter 5
Figure 5.1: First Stage Pass-through Variables.............................................................35
Figure 5.2: First Stage Impulse Response for One Standard Deviation Permanent and
Transitory Shocks With Johansen Normalised Coefficient: Full Sample ....................40
Figure 5.3: First Stage Impulse Response for One Standard Deviation Permanent and
Transitory Shocks With DOLS Estimates: 1983Q2 - 1993Q1 .....................................43
Figure 5.4: First Stage Impulse Responses for One Standard Deviation Permanent and
Transitory Shocks With DOLS Estimates: 1993Q2 - 2010Q1 .....................................45
Figure 5.5: First Stage Pass-through Rolling Window on DOLS Coefficients ............48
Chapter 6
Figure 6.1: Second Stage Pass-through Variables ........................................................50
Figure 6.2: Impulse Responses for One Standard Deviation Permanent and Transitory
Shocks With DOLS Estimates: Full Sample ................................................................55
Shocks With DOLS Estimates: 1983Q2 - 1993Q1.......................................................58
Shocks With DOLS Estimates: 1993Q2 - 2010Q1.......................................................60
Figure 6.5: Second Stage Pass-through Rolling Window on DOLS Coefficients........64
Chapter 7
Shocks With Johansen Normalised Coefficients: Full Sample.....................................70
Figure 7.2: Combined Stage Pass-through Rolling Window on DOLS Coefficients...74

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LIST OF FIGURES (CONT.)
Appendices
Figure A4.1: First Stage Pass-through Impulse Response Functions From VAR(3):
Full Sample...................................................................................................................93
1983Q2-1993Q1 ...........................................................................................................93
1993Q2-2010Q1 ...........................................................................................................94
Figure A4.4: Second Stage Pass-through Impulse Response Functions From VAR(3):
Full Sample...................................................................................................................94
Figure A4.5: Second Stage Pass-through Impulse Response Functions From VAR(3)
With Ordering Implied By DOLS Estimates: 1983Q2-1993Q1...................................95
With Ordering Implied By Johansen Normalised Coefficients: 1983Q2-1993Q1.......95
With Ordering Implied By Johansen Normalised Coefficients: 1993Q2-2010Q1.......96
Figure A4.8: Combined Stage Pass-through Impulse Response Functions From VAR(3)
With Ordering Implied By Johansen Normalised Coefficients: Full Sample...............97
Figure A6.1: First Stage Pass-through Impulse Response Functions on VECM(2)
Implied By Johansen Normalised Estimates: Full Sample.........................................102
Figure A6.2: Second Stage Pass-through Impulse Response Functions on VECM(2)
Implied By DOLS Estimates: Full Sample.................................................................103
Figure A6.3: Impulse Responses for One Standard Deviation Permanent and
Transitory Shocks With Johansen Normalised Coefficients: 1983Q2-1993Q1 .........103
Figure A7.1: First Stage Johansen Normalised Cointegrating Coefficients...............104
Figure A7.2: Second Stage Johansen Normalised Cointegrating Coefficients...........104
Figure A7.3: Combined Stage Johansen Normalised Cointegrating Coefficients......104

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Abstract
This thesis seeks to address the resilience of the import price pass-through to the retail
price (second stage) despite significant pass-through from the exchange rate to the
import price (first stage). Specifically, this "exchange rate puzzle" is addressed by
extending a theoretical mark-up model into a cointegrated vector autoregressive
framework to assess whether the mark-up on input cost plays an increasing role in the
exchange rate pass-through during the past decades. If so, is the decline in the
exchange rate pass-through triggered by inflation targeting?
The main contribution to the previous literature involves the decomposition of
permanent and transitory shocks with structural identification under weak exogeneity
in a system framework, which complements the typical single-equation results seen in
the exchange rate literature.
Three main results are concluded. Firstly, the first stage pass-through confirms the
lack of second stage pass-through is not a result of sluggish response in the first stage.
Secondly, second stage pass-through result is much slower and low in magnitude
compared to the first stage, which suggests the mark-up is increasingly persistent in
conjunction with large fluctuations during the inflation targeting periods. Additionally,
retail price becomes less persistent after the inflation targeting policy, which provided
consistent evidence that inflation targeting triggers these behaviours. Lastly, from the
combined stage pass-through, the direct impact of the exchange rate pass-through on
the retail price is found to be higher than the second stage. Nevertheless, mark-up on
cost is still an important factor in the direct pass-through of exchange rate to the retail
price. Although, the robustness subsample test cannot confirm the results from the
combined stage.

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1. Introduction
Since the floatation of the Australian dollar during the 1980s, exchange rate shocks
has always been an external shock that influences the domestic inflation levels.
However, with the past two decades of inflation targeting implemented by the Reserve
Bank of Australia (RBA), inflation was kept at a low level in the face of inflationary
pressure derived from fluctuations in the exchange rate during economic booms in the
business cycle.
A recent bulletin article published by Chung et al. (2011) from the RBA described the
recent surge in the exchange rate pass-through to retail price via the imported
consumption channel, particularly for highly tradeable consumption goods. In a mark-
up framework where retail price is in proportion to the import price and the input cost
with an additional mark-up, D'Arcy et al. (2012) provided the fact that "...only half of
the final price of retail goods is attributable to the cost of producing these items. The
other half is the cost of distributing these items...". These two articles highlighted the
importance of cost. Therefore, the mark-up on costs appears to be a key determinant in
the exchange rate pass-through.
Figure 1.1: Plot of Exchange Rate, Import Price, and Retail Price
40
50
60
70
80
90
100
110
120
84 86 88 90 92 94 96 98 00 02 04 06 08
TWI PD RPI

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From Figure 1.11
, during the episodes of depreciation between the late 1984 to mid
1986, the import price over-the-docks shows potential influences over the retail price
of consumption imports2
. During the episodes of depreciation occurred in the early
1990s, the retail price seem to be less responsive to changes in import price. In
addition, the announcement of inflation targeting by RBA in mid 1993 marked by the
dashed line seems to have caused retail price to adjust to changes in the import price.
After mid 2002, the response of retail price is insensitive to the episodes of sustained
appreciation, which consequently caused a general decline in the import price.
Particularly, during the height of global financial crisis, retail price is unaffected
despite sharp changes in the import price. Thus, the alighted "exchange rate puzzle"
involve the insensitivity of retail import price in respond to significant movements in
the exchange rate, even though, the response of import price to fluctuations in the
exchange rate is quick and large in magnitude.
Three possible explanations are mentioned by Murray (2008). One possible reason is
due to the claim made by Taylor (2000) who associated the decline in pass-through to
the global stabilisation of inflation. Another reason mentioned is due to changes in
composition of trade. Campa et al. (2005) and related studies unearthed evidence
towards an increase in differentiated manufacture goods caused by an increase in price
discrimination across markets. The last reason is attributed to an increased
globalisation and the role played by emerging countries.
Amongst these reasons, Dwyer and Leong (2001) explained the decline in pass-
through is due to structural change in Australia's inflation process, however, they did
1
TWI = Trade Weighted Index, PD = Imported Price of Consumption Over-the-docks, RPI = Retail
Price of Consumption Goods.
2
Exchange rate, import price over-the-docks, and retail price form the consumption portion of imports
derived from aggregated data. For more details, please refer to Appendix 1: Data Construction.

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not find a statistically significant result of their claim. Another possibility mentioned
by Dwyer and Lam (1994) is due to the ability of retailers in varying their mark-up on
input costs, thereby, absorbs some of the fluctuations. Particularly, they argue the
tendency of addressing the second stage as being "incomplete" when retail price
responds less than one-for-one with import price over-the-docks.
Despite various theories in response to the exchange rate puzzle proposed in the pass-
through literature, there is still insufficient empirical evidence that evaluates
Australia's exchange rate pass-through to the domestic inflation. This thesis
contributes to the pass-through literature by empirically evaluating the dynamic
relationships of exchange rate shocks and the response to the key domestic variables.
Typically, the main research question that this thesis will address is; does mark-up on
cost of input serve to explain the exchange rate puzzle? If so, is the decline in pass-
through over the recent decades a result of the increasing role played by the mark-up
on costs triggered by the RBA's inflation targeting policy?
In response to the stated research question, there are important implications if mark-up
on cost are found to play a significant role in exchange rate pass-through. Better
understanding of the pass-through to the retail price enables central bank to improve
forecast in future inflation levels, which increase the efficiency in the implementation
of monetary policy aim to control inflation within 2-3% target level in the medium
terms. If shocks are insulated due to a more fluctuating and persistent mark-up, which
prevents the penetration to retail price, then, policy that target exchange rates should
be kept minimum to avoid any adverse consequences on the domestic output.
The pass-through of exchange rate to the retail price is typically decomposed into two
stages. The first stage empirically confirms exchange rate shock does transmit to the

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import price at a relatively fast speed and significant in magnitude. Consequently, the
research question is addressed in the second stage of pass-through.
Despite the abundance of single-equation framework in the existing literature, a
structural system of linear equations is used instead for each pass-through stage.
Norman and Richards (2010) discussed the drawbacks of single-equation and its
limitations. Clearly, one of the advantages of using a structural model is the ability to
impose certain theoretical restrictions that most economists agree in the long-run.
Additionally, a Structural Vector Error Correction Model (SVECM) has the advantage
in decomposition of key pass-through variables in to permanent and transitory shocks.
The second stage informs us whether the mark-up is important when first stage pass-
through is described as near complete. Second stage results shows the pass-through of
import price to the retail price is extremely, slow and small in magnitude. This implies
that the fluctuations in mark-up are large and persistent in the long-run. However, the
input cost shocks (mark-up) in response to the retail price were not captured in the full
sample due to the shock been transitory.
This drawback motivated the combined stage pass-through in a four-variables SVECM
which directly traces the relationship between the retail price and the exchange rate.
Once the exchange rate (and the world price) is explicitly included into the system, the
response of retail price to input costs is slow and moderate in magnitude. Furthermore,
the response of retail price to exchange rate shock more than doubles in the long-run
equilibrium when compared to the second stage. This reflects the price adjustment to
the changes in the exchange rate directly. In conclusion, varying mark-up plays an
absorptive role which explains the resistance in the pass-through of import price to the
retail price, when pass-through of exchange rate to the domestic import price is high.

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2. Literature Review
Exchange rate pass-through has been a widely researched area. The theoretical
literature involves two approaches, namely, the partial equilibrium and the general
equilibrium. In this chapter, a brief overview of the Australian experience in pass-
through literature will be explored. This is followed by a section of the two different
approaches. The focus of the literature review is on the exchange rate pass-through to
inflation either via import channel or export channel. Finally, empirical literature is
discussed to shed light on the model identification strategy used in this study.
2.1 Australian Pass-through Literature
A vast body of pass-through literature focus on industrialised economies; however,
there are limited studies in the Australian pass-through literature. This is due to the
unavailability of data that drives the empirical work. For example, a popular proxy
used for controlling demand pressure of the destination country can be difficult to
obtain for Australia. Despite this issue, a few key papers triumph in exploring the
relationship between the pass-through of the exchange rate and either the import price
of particular domestic industry or that of the export price. A widely cited paper written
by Menon (1993) examined the exchange rate pass-through to Australian imported
motor vehicles. The author found the short run pass-through of exchange rate was 70%
and long run pass-through estimated to be 80%. These estimates are slightly higher
compared to that of other industrialised economies.
Despite various studies on the exchange rate pass-through to the imported manufacture
goods prices, both Menon (1992) and Swift (1998) also examined the export price of
Australian manufactured goods in the 1980s. However, Menon (1992) used
disaggregated export dataset for manufactured goods, while Swift (1998) explored the

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exchange rate pass-through with aggregated exports. Another similarity is the mark-up
analytical framework, which enables retailers to vary their mark-up in response to the
exchange rate shock. Menon (1992) concluded a varying degree of pass-through
effects in different industries. This conclusion is based on complete import price pass-
through with the small country assumption of the Australian economy. Thus, the terms
of trade in the domestic economy is insulated from any changes movements in the
exchange rates. Swift (1998) concluded that in the long run, 60% of the exchange rate
pass-through to the Australian aggregate exports. This conclusion supports Menon
(1992) on the small country assumption for the Australian aggregated exports.
A series of RBA research discussion papers focus on the issue of exchange rate pass-
through via the imported price channel. Dwyer et al. (1993), however, explored the
pass-through effects of exchange rate movements for both imported price and export
price of manufactured traded goods. They also tested the small country assumption for
Australia with the inclusion of second stage pass-through. Menon (1992), Menon
(1993), and Swift (1998) focused their studies solely on the first stage of pass-through3
.
In the long run, in comparison to Menon (1993), Dwyer et al. (1993) found the first
stage exchange rate pass-through to be complete and rapid. However, in the short run,
the pass-through effect completes within one year. Furthermore, both studies find a
reduction in the speed of the exchange rate pass-through to exports and detect the
change in pricing patterns.
During the depreciation occurred in the 1990s, Dwyer and Leong (2001) addressed the
question of whether the periods of stable inflation was the result of favourable shocks
to the macroeconomy or was it a fundamental structural change in the Australian
inflation process. Through a mark-up framework, they found statistically insignificant
3
The second stage pass-through to export was not considered by Menon (1993).

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decline in the speed of adjustment back towards long-run equilibrium. Nevertheless,
they conclude Australia is far from perfect insulation against exchange rate shocks and
any other types of external shocks that are large in magnitude and persistent in nature.
In contrast to the error correction type methodology employed in the Australian
literature, Heath et al. (2004) used Dynamic Ordinary Least Square (DOLS) to
estimate the two pass-through stages in a mark-up framework. They incorporated leads,
lags, and contemporaneous terms for the growth in import price, unit labour cost, and
output gap in the two pass-through stages. Compared to Dwyer and Leong (2001) with
insignificant decline in pass-through, Heath et al. (2004) found that there has been a
large scale decline in the pass-through of exchange rate to the consumer price. This
pattern correlates to the global trend of low and stable inflation environment.
Finally, Dwyer and Lam (1994) focused their attention to the second stage pass-
through. Empirical research in Australia has limited break-through in the second stage
pass-through due to data constraints. However, they shed new light with the
construction of their own costs and margins. They addressed the pass-through in two
stages with a mark-up model estimated on the grounds of Unrestricted Error
Correction Model (UECM). They confirmed that the first stage pass-through for
Australia is complete in the long-run. Furthermore, they concluded that the second
stage was also complete.
2.2 Partial Equilibrium Approach
Partial equilibrium approach has been the main theoretical underpinning of the early
literature in the exchange rate pass-through. This approach has roots that originated
from the Law Of One Price (LOOP). LOOP states that the price of all traded goods
should be the same across all countries when currency is expressed under a common

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denominator4
. Bailliu et al. (2010) described three main pitfalls of using partial
equilibrium approach. Firstly, this approach assumes exchange rates are weakly
exogenous based on reduced-form models. Secondly, the lack of structure will cause
imprecision in the interpretation of the exchange rate pass-through coefficients. Lastly,
the reduced form model motivated under partial equilibrium approach fails to
distinguish between the different effects on the economy caused by different nature of
the shocks.
Ihrig et al. (2006) demonstrated the approach in an empirical framework using an
algorithm adapted from Henry and Krolzing (2001) for the G7 countries. The author
concluded a general decline in the exchange rate pass-through to the import price for
all countries studied, except Canada with almost complete pass-through. Moreover, six
out of the seven countries studied shows a decline in the pass-through to the consumer
price.
Goldberg and Knetter (1997) summarised the micro-foundation that relates to this
strand of literature. Specifically, this strand of literature attributes the incomplete pass-
through to an included cost measure. This input cost enables exporters to vary their
mark-up that results in the deviation from perfect competition. The treatment of the
input cost as an observable causes downward bias in the exchange rate pass-through
coefficient. Hence, overstates the variation in the mark-up. Similar issue was raised in
Dwyer and Lam (1994) that the complete pass-through must be less than unity.
Krugman (1987) initiated a series of pricing-to-market studies in an attempt to mix
micro-foundation into the exchange rate pass-through literature. The fundamental
difference is due to segmentation in markets, thus, individual export prices are
determined in each of the segmented market respectively.
4
Exchange rate here defined as in unit of home country currency to the foreign currency.

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Marazzi et al. (2005) provided a recent example of pricing-to-market. The purpose
was to maximise profit from the total sum of all markets for a differentiated product
subject to given constraints. Their results support the decline in the exchange rate
pass-through to the US import prices, which is consistent with Heath et al. (2004) for
the Australian experience and Ihrig et al. (2006) for the G7 countries. Furthermore,
they found that the speed of adjustment in the pass-through coefficients on foreign
export price was rapid.
2.3 General Equilibrium Approach
Another strand of literature focuses on a general equilibrium approach. This approach
assumes nominal prices are sticky in either the import or the export country. Two
pricing techniques need to be defined. Producer currency pricing (PCP) involves
imports priced in the exporters' currency, while local currency pricing (LCP) involves
imports priced in the importers' currency5
. Examples of this approach includes Hooper
and Mann (1989), Campa and Goldberg (2002), Campa et al. (2005), and Campa and
Goldberg (2006b).
Hooper and Mann (1989) extended the partial equilibrium to a general equilibrium
approach. The authors incorporated other factors in to the costs as a function and other
factors on top of the partial equilibrium type model. In the long-run, they found US
exchange rate pass-through to import price was approximately 50 to 60%, and in the
short run, it was approximately 20%. These findings are robust across time,
specifications of the three models, and their estimations.
Three related studies were conducted by Campa and Goldberg (2002), Campa et al.
(2005), and Campa and Goldberg (2006b). The first two studies explored the exchange
5
For more details on pricing techniques, see Devereus and Engle (2003).

21
rate pass-through to imports. Campa and Goldberg (2002) found evidence of a decline
in the pass-through within some of the OECD countries studied. Particularly, they
examined the LCP and PCP for 25 OCED countries, which they concluded most
countries are neither LCP nor PCP. Furthermore, they found the shift towards
manufacturing imports had contributed significantly to the decline in pass-through for
approximately half of the OCED countries.
Campa et al. (2005) found similar results. The long-run elasticity of pass-through is
approximately 80% in aggregate. In the short run, the pass-through effect is reduced to
approximately 66%, and 56% on average amongst industries. This is consistent to
Campa and Goldberg (2002) with average short-run estimates of 60% pass-through,
and 80% pass-through over the long-run. Campa and Goldberg (2006b) investigated
the exchange rate pass-through to consumer prices through different types of bordered
import goods prices. By calibrating their empirical model, they verified the source of
the change in exchange rate pass-through to the consumer price was associated with
the imported input more than the expenditures. They further found that manufacturing
was measured more precisely than other one-digit Standard International Trade
Classifications (SITC).
A deviation of the general equilibrium approach involves incorporating micro-
foundation into the new open-economy macroeconomic models. Taylor (2000) utilised
this framework and examined an alternative explanation for the decline in pass-
through. The author attributes the decline to the recent stabilisation and low inflation
environment seen globally. Under monopolistic competition, costs derive from the
exchange rate movements and increased competitive environment, thus, these costs is
interpreted as a reduction in the firms' pricing power under low and stable inflation
environment. Furthermore, the author concluded based on an empirically staggered

22
pricing model that the lower the persistence of inflation, the lower the degree of
pricing power.
Choudhri and Hakura (2006) provided one direct support to Taylor (2000). The
authors used a dataset of 71 countries, in the periods between 1979 to 2000, to test the
claim made by Taylor (2000). They concluded a strong and significant relationship
between inflation and the first stage of pass-through. However, they implicitly
assumed PCP, thus, implied that the exchange rate pass-through is independent of
inflation. Junttila and Korhonen (2012) is a recent study conducted based on Choudhri
and Hakura (2006). In contrast the assumption of made by Choudhri and Hakura
(2006), they assumed dependence between the pricing decision of exchange rate pass-
through to the import prices and inflation regime of the destination country. The
authors used a dataset of 9 OECD countries, based on nonlinear estimation techniques,
and also confirmed the claim made by Taylor (2000).
2.4 Empirical-based Literature
Besides theoretical studies, empirical-based studies have also been undertaken in the
exchange rate pass-through literature. Vast majority of the empirical studies focused
on a Structural Vector Autoregression (SVAR) framework that explores the exchange
rate pass-through to a distribution chain of pricing. Some examples of studies includes
McCarthy (2000), Hahn (2003), and Faruqee (2004).
McCathy (2000) and Hahn (2003) both examined the exchange rate pass-through to
domestic inflation via the imported price channel in a SVAR framework. However,
McCarthy (2000) explored the impact of the import price and the exchange rate shocks
on Producer Price Index (PPI) and Consumer Price Index (CPI) in a selected list of
advanced economies. The author utilised the short-run recursive identification scheme

23
(Choleski decomposition) to identify structural shocks and concluded the pass-through
of exchange rate shocks to import price is far from complete. Particularly, the pass-
through to PPI and CPI were moderately stronger than the import price. The author
attributed this decline to the successful implementation of monetary policy by central
bankers.
Hahn (2003) investigated the impact of oil price shocks, exchange rate shocks, and
non-oil import price shocks on a distribution chain of pricing of import prices,
producer prices, and consumer prices in the Euro Area. The author found the size and
speed of non-oil import price pass-through was the quickest and the exchange rate
pass-through ranked second. Similarly, the results are broadly consistent to the modest
pass-through effect detected in McCarthy (2000). Faruqee (2004) supported Hahn
(2003) claim of exchange rate pass-through was the quickest for import prices. The
author also suggested that the apparent incomplete pass-through can be explained
through the adjustment of costs borne by local retailers. This is consistent to Dwyer
and Lam (1993) where the authors analysed this claim formally in the second stage
pass-through for Australia.
Furthermore, Hüfner and Schröder (2002) utilised a VECM framework that
investigated the exchange rate pass-through to consumer prices for the selected Euro
countries. In contrast to most of the other studies, one fundamental conclusion was that
if inflation environment changes, consumer price is influenced by the exchange rate
pass-through. This provided support to Taylor (2000).
This thesis will employ a SVECM with identification under weak exogeneity. Fisher
and Huh (2012) used Pagan and Pearson (2008) framework and applied it to Gonzalo
and Ng (2001) to prove that if known permanent shocks derived are weakly exogenous,

24
then the set of restrictions imposed on the cointegrating equation just identify the
structural model. Additionally, Choleski decomposition ordering imposes the same
restrictions equivalent to the set of implied structural cointegration restrictions. Lettau
and Ludvigson (2004) is an application of the above identification strategy. The
authors used the permanent and transitory decomposition under weak exogeneity to
examine the relationship between consumption, asset wealth, and labour earnings.

25
3. Theoretical Model
This chapter provides the theoretical underpinning of the mark-up model for the
inflation process. The model closely follows the analytical framework outlined by
Dwyer and Lam (1994) and Hooper and Mann (1989). This type of mark-up
framework under partial equilibrium has been extensively used in all of the studies
undertaken on Australia in the exchange rate pass-through literature. Typically, the
first stage pass-through has been extensively examined. However, Dwyer and Lam
(1994) explains the importance of second stage pass-through due to the role played by
the retail price of imported consumption goods in the formulation of the inflation
process.
Under perfect competition, the first stage of pass-through can be described by the Law
of One Price. Thus, the following equation represents the first stage pass-through:
D W
P P E (3.1)
where,
denotes the domestic import price index over-the-docks
denotes the world export price
denotes the exchange rate in foreign currency per unit of domestic currency
Under the Law of One Price, the elasticity of import price over-the-docks with respect
to exchange rate can be described as complete in the long-run, although, the short-run
pass-through does not need to be complete.
1
D
D
dP E
dE P
  (3.2)

26
In addition, the world export price acts as a proxy for foreign mark-up on top of their
export price. This relationship is represented as the following:
W f f
P C (3.3)
where,
denotes the foreign mark-up on imported production costs
denotes the foreign import cost of production denominated in foreign currency
In an imperfectly competitive setting, second stage pass-through involves the elasticity
of the retail imported price of consumption goods with that of the after-tax imported
price, over-the-docks. The relationship can be expressed as a function of the after-tax
imported price with a mark-up on costs borne by the domestic importers and retailers:
1
( ) ( )D D C D
R T P P 

  (3.4)
where,
denotes the retail price of imported consumption goods
denotes the domestic costs borne by importers and retailers
denotes the domestic mark-up on costs
denotes an index of
Equation (3.4) shows that the retail price is a weighted function of the imported price
and the domestic costs. That is, the movement in retail price is either attributed to the
movements in the tariff-adjusted imported price or the costs faced by retailers and
importers scaled by the mark-up .

27
Moreover, the pass-through will be dependent on the share of import price, , due to
the ability of retailers to vary their mark-up in response to the first stage exchange rate
pass-through. Theoretically, the elasticity of the second-stage is less than unity, since
the share of import price is only a portion of the total costs faced by retailers2
.
D D
D D
dR P
dP R
  (3.5)
Combined pass-through of the first and second stage is achieved through substituting
equation (3.1) into (3.4).
1
( ) ( )D W C D
R T P E P 

   (3.6)
Equation (3.6) traces directly the full path of exchange rate movements to the domestic
retail price of consumption goods via the imported over-the-docks price. The channels
considered are the extent of the share given to costs, the variation of mark-ups by
domestic retailers, and the variation in foreign mark-up embedded in the world export
price.
1
Total costs include the major costs defined in the Appendix 1 with additional fixed costs not included,
due to their insignificance and invariability compared to the major costs defined.

28
4. Econometric Methodology
This chapter begins with a transformation of the previous theoretical model into an
empirical framework that enables us to answer the key research questions presented
previously. A cointegrated VAR framework (VECM) will be used to trace out the
speed and magnitude of exchange rate shocks in response to key variables in the
model. There are several advantages of using a cointegrated VAR framework. Firstly,
all variables are treated as potentially endogenous, thus, eliminating the assumption of
weak exogeneity in the exchange rate. Additionally, a structural model captures the
contemporaneous feedback effects amongst the variables. Lastly, the model allows the
exchange rate pass-through effect to vary with the nature of the shocks determined
implicitly from the data. Thus, the speed and magnitude of pass-through can differ
significantly.
4.1 Structural Vector Error Correction Model
This section explains the SVECM framework with the methods of identification used
to recover structural shocks to be discussed in the later sections. In particular, the
effects of speed and magnitude of a structural shock on the retail price can be
determined through impulse response functions and forecast error variance
decompositions.
A SVECM is distinguished from a VECM due to the inclusion of a feedback effect
which captures the contemporaneous relationships amongst the potential endogenous
variables. The general form of a SVECM is represented in the following matrix
notation:
0 1 1)t t t tC Y Y L Y        (4.1)

29
where,
tY denotes 1n vector of the first difference of endogenous variables
1tY  denotes 1n vector of the level of endogenous variables
0C denotes n n matrix of parameters that captures the contemporaneous effect of a
change in a endogenous variable to the other
t denotes 1n vector of the structural form innovations
 denotes n n matrix of coefficients that adjust from the deviation back to the long-
run equilibrium
)L denotes n n lagged matrix of parameters defined on the vector autoregression,
1tY
Direct estimation of the above model is not possible without imposing sufficient
identifying restrictions. The problem can be eliminated by transforming (4.1) into a
reduced-form VECM. The following matrix notations show the reduced-form model:
1 1)t t t tY Y L Y u 
       (4.2)
where,
tu denotes 1n vector of the reduced-form disturbances
From the structural equation, the relationships with the reduced form can be shown as
following:
1
0C
   (4.3)

30
1
0) )L C L
   (4.4)
1
0t tu C 
 (4.5)
where,
1
0 0B C
 denotes the impact multiplier matrix
Furthermore,  is represented by the following:
  (4.6)
where, denotes n r matrix coefficients that adjusts to deviations from the long-run
equilibrium and  is the transpose of a n r matrix of restrictions imposed on the
cointegrating equation. r is the number of cointegrating relationships amongst the
endogenous variables and is the rank of  . The reduced rank means that there exist
k n r  common stochastic trend/s. In particular, the structural shocks, t , can be
decomposed into ( , )k r
t t   where k
t represents a vector of 1k  permanent structural
shocks and r
t represents a vector of 1r transitory structural shocks. Thus, both
permanent and transitory structural shocks needs to be identified through plausible
restrictions, which are discussed in the following section.
4.2 Identification Under Weak Exogeneity
The presence of cointegration allows the identification of permanent shocks through
imposing restrictions on the multiplier matrix of the reduced form disturbances. Thus,
the restrictions implied by cointegration are sufficient to identify permanent and
transitory shocks. However, these restrictions imposed by cointegration do not help to
identify each individual permanent shock, but rather, the combined permanent

31
components. Furthermore, the transitory shock is exactly identified with the
assumption that the innovation from the transitory component is orthogonal to the
innovations of permanent components.
Additional identification assumptions must be imposed to identify each permanent
shock individually. In particular, Gonzalo and Ng (2001) formulated one identification
procedure3
. Furthermore, Fisher and Huh (2012) reinterpreted Gonzalo and Ng (2001)
procedure in the Pagan and Pesaran (2008) framework, which demonstrated the
permanent-transitory decomposition of a SVECM through imposing the following
identifying restrictions:
0k krB   (4.7)
where,
kB denotes the first k rows of the 0B impact multiplier matrix
0kr denotes k r zero matrix
Equation 4.8 presents the Permanent-Transitory decomposition of the  matrix of the
SVECM model with the imposed restrictions implied by Equation 4.7:
0kr
rB


 
   
 
(4.8)
where,
rB denotes the r rows of 0B impact multiplier matrix
3
Please refer to Gonzalo and Ng (2001) for more details of their definition in impact multiplier
matrix.

32
To allow exact identification of each of the permanent shocks and the transitory shock,
additional restrictions must be applied to the impact matrix, 0B , in a way that
disentangle the two permanent shocks. Sims (1980) first proposed the Choleski
decomposition which adopted a recursive structure to identify the impact multiplier
matrix. However, the lower triangular setting implies that, dependent on the ordering,
some variables by construction do not contemporaneously affect each other.
In order to achieve the identification of the impact multiplier matrix, variance-
covariance matrix must be defined. The variance-covariance matrix of the structural
innovations and the reduced form disturbances is denoted as the following:
1 1
0 0( )u t tE C C        (4.9)
where:
u denotes ( )t tE u u  as the variance-covariance matrix of reduced form disturbance
 denotes ( )t tE    as the variance-covariance matrix of structural innovation
The assumption of orthogonal shocks allows the covariance of variance-covariance
matrix to be restricted to zero, with unit variances in the main diagonal. Thus,
Equation (4.9) is re-written as the following:
1 1
0 0u C C    (4.10)
Fisher and Huh (2012) proposed that exact identification of SVECM is achieved
through the Choleski decomposition of variance-covariance matrix, with the set of
imposed zero contemporaneous restrictions, which are equivalent to the structural
cointegration restrictions.

33
5. First Stage Pass-through: Exchange Rate to Import Price
The main objective of this chapter is to determine whether exchange rate shocks
transmit to import prices over-the-docks. Consequently, the speed, magnitude and
importance of an exchange rate shock are examined through impulse response
functions and variance error decompositions. Thus, this chapter provides empirical
evidence on the first stage pass-through necessary to determine the degree of exchange
rate pass-through to retail prices in the second stage. The chapter concludes with a
robustness check across various subsamples and an analysis of the stability of the
long-run coefficients.
5.1 Data Description and Properties
The first stage pass-through variables are: the imported price of consumption goods
over-the-docks ( )D
tp , the nominal effective exchange rate ( )te , and the world export
price ( )W
tp . All data are quarterly, in natural logarithms, and the full sample is from
Q2 1983 to Q1 20101
.
The import price of consumption goods over-the-docks is the price of imports before
any distributional costs, sales, or tariffs are imposed. Thus, it represents the raw price
of imported goods at the point of entry, with no additional costs being imposed.
Additionally, the import price was measured "free-on-board", which implies only the
consumption portion of the imported price was extracted.
Two components are required in the construction of the series, both sourced from the
Australian Bureau of Statistics (ABS) website. The first component is the import price
index that includes both consumption and non-consumption goods. The second
1
Data in yearly frequency are transformed to quarterly frequency by geometric linear interpolation. See
the Appendix1:Data Construction for more details.

34
involves a proxy for the weights used to isolate the consumption portion of the
imported price index. These weights are formed from merchandise imports using the
SITC at the 1 and 2 digit levels. This approach is broadly consistent with Dwyer and
Lam (1994). They removed the portion of the import price index that was attributed to
non-consumption goods and the weights were held constant, without any
normalisations.
The nominal effective exchange rate is constructed from the monthly nominal TWI
and transformed into quarterly figures by retaining the last month of each quarter. The
nominal TWI is sourced from the RBA Statistical Tables2
. The data sample is
available for the full sample period.
To proxy for the world export price, the export price index is used for all countries3
. If
the export price index is absent for a particular country, the unit value index is
substituted. Both sets of indexes are sourced from the World Bank and OECD
websites. The index for the world price is constructed as a weighted average of export
prices or unit values of all Australia's major trading partners.
In conjunction to the export price index, Dwyer and Lam (1994) also incorporated
price series for consumer exports for those countries with the available data in
construction of the world price series. However, instead of using TWI as weights, they
used the four-digit Australian Standard Industrial Classification (ASIC) data to
construct the weights4
.
2
Please refer to the Bibliography for the link to the RBA Statistical Table.
3
Although, the export price for consumption goods should be used instead, however, actual export price
are unavailable for half of the countries, hence, export price index is used for consistency.
4
TWI weights for each of Australia's major trading partners are reported in Appendix 1:Data
Construction.

35
Figure 5.1 plots the first stage pass-through variables. We can infer from the plots that
all series contain non-zero intercept, although, linear deterministic trends are
debateable.
Figure 5.1: First Stage Pass-through Variables
Before any modelling technique is undertaken, the data properties are first examined.
Preliminary tests for unit roots and cointegration will play a vital role in motivating the
use of a SVECM.
Table 5.1 shows the result from the Augmented Dickey-Fuller (ADF) test. The ADF
test confirms that all series for first stage pass-through have unit roots and are I(1).
Both the Engle-Granger (EG) test and the Johansen cointegration test are used to test
for cointegration. Table 5.2 outlines the EG test with each variable used in turn as the
4.0
4.2
4.4
4.6
4.8
5.0
84 86 88 90 92 94 96 98 00 02 04 06 08
Import Price Over-the-docks
4.0
4.2
4.4
4.6
4.8
5.0
84 86 88 90 92 94 96 98 00 02 04 06 08
Nominal Effective Exchange Rate
4.0
4.2
4.4
4.6
4.8
5.0
84 86 88 90 92 94 96 98 00 02 04 06 08
World Export Price

36
Table 5.1: First Stage Augmented Dickey-Fuller Test Results
Variables
Level First Differences
Lags
Test
Stats
CV at
5%
P-
value
Lags
Test
Stats
CV at
5%
P-
value
D
tp 6 -2.72 -3.45 0.23 5 -5.01 -2.89 0.00
te 11 -2.23 -2.89 0.20 11 -3.77 -2.89 0.00
W
tp 6 -1.77 -3.45 0.71 0 -5.59 -2.89 0.00
Lag length selected based on t-statistics with maximum lag length of 12.
Constant and linear trend only included in the levels of import price and world price.
P-value is based on MacKinnon (1996) one sided P-value.
Table 5.2: First Stage Pass-through Engle-Granger Test Results
Dependent Variable Lags t-statistics P-value
D
tp 0 -3.77 0.06
te 0 -4.01 0.03
W
tp 0 -2.43 0.53
Selection of lags based on t-statistics with 5 maximum included lags.
Null hypothesis: No cointegration
Table 5.3: First Stage Pass-through Johansen Cointegration Test Results
Level VAR (3)
Trace Maximum Eigenvalue
Rank Test
statistics
5% CV P-value
Test
statistics
5% CV P-value
0 37.59* 35.19 0.03 23.76* 22.30 0.03
1 13.83 20.26 0.30 10.53 15.89 0.29
2 3.3 9.16 0.53 3.30 9.16 0.53
*Statistically significant at 5% level.
Intercept in CE with no deterministic trend included in both CE and VAR.
Table 5.4: First Stage Pass-through Estimation Results
Variables
Normalised CI
Coefficients
OLS DOLS
Coefficients Coefficients P-value
te -0.93
(0.18)
-1.21
-1.18
(0.22)
0.00
W
tp
0.72
(0.25)
0.72
0.70
(0.15)
0.00
Constant is included in regression but omitted from report.
DOLS using AIC (Max lag length 5) lag and lead both equal to three with HAC standard errors (fixed
4 lags).
Standard errors reported in parenthesis.
The reported long-run elasticity are in the form of 1 1 1 2 1
D W
t t tp e p     .

37
dependent variable. We conclude from the result that cointegration is present for the
first stage pass-through.
Before the implementation of the Johansen procedure, the number of lags included in
the test is guided by a set of information criterions from the estimation of the levels
VAR for each pass-through stage. The residuals of the selected model are checked for
serial correlation5
.
The Johansen cointegration test results are shown in Table 5.3. We can infer one
cointegrating relationship exists amongst the first stage pass-through variables from
both the trace and maximum eigenvalue test statistics at the 5% level of significance.
The normalised cointegrating coefficients are used to compare with those estimates
obtained from OLS and Dynamic OLS (DOLS) regressions6
. Results from Table 5.4
suggest the long-run elasticity of exchange rate is approximately 0.93, which is close
to the theoretical complete pass-through. This is broadly consistent to the coefficients
on exchange rates for both OLS and DOLS, although these estimates suggest slightly
higher than 100% pass-through on the exchange rate.
5.2 Model Estimation and Identification
The Johansen cointegration test indicates the presence of one cointegrating
relationship. However, in order to determine which variables are attribute to the
transitory shock and permanent shocks, estimation of reduced-form VECM(2) is
drawn7
. From the model estimation presented in Table 5.5, the results show the
adjustment coefficients on the error correction term for the growth in world price is
5
Please refer to Appendix 2 for details on VAR lag length selection.
6
The coefficient on import price variable, , is normalised to one in order to determine the long-run
elasticity of the exchange rate and the world price.
7
VECM(2) is constructed using 2 lags of differenced endogenous variables with an intercept in the
cointegrating vector, but no other deterministic components.

38
statistically insignificant at a 5% level. Additionally, both import price and exchange
rate show statistically significant results at a 5% level, however, exchange rate shows
insignificant result at a 1% level. According to the most significant adjustment
coefficient on import price, we infer that the transitory shock is derived from import
price and the two permanent shocks are derived from world price and exchange rate.
Table 5.5: First Stage Adjustment Coefficients Implied By Johansen Normalised
Coefficients on Error Correction of VECM(2)
Dependent
Variable
Independent
Variables
Coefficient t-statistics
D
tp 1tec 
-0.18
(0.04)
-4.91*
te 0.13
(0.05)
2.43**
W
tp
0.003
(0.01)
0.33
* Statistically significant at 1%.
** Statistically significant at 5%.
Error correction term is defined as 1 1 1 10.93 0.72 4.96D W
t t t tec p e p       using normalised
cointegrating coefficient from the Johansen test presented in Table 5.4.
Application of the identification scheme under weak exogeneity outlined in the
econometrics methodology chapter is now implemented for the first stage. The
structural model for the first stage pass-through is represented as follows:
 
0,11 0,12 0,13 1 1
0,21 0,22 0,23 1 2 3 1 2
0,31 0,32 0,33 1 3
W
D
W p D
t t t
e
t t t
D Wp
t t t
p b b b p
e b b b e
p b b b p
 
    




       
                  
              
(5.1)
The identifying structural cointegration restrictions can be applied to the first stage
pass-through:
0,11 0,12 0,13
0,21 0,22 0,23
0
0
W
D
p
e
p
b b b
b b b



 
    
    
    
 
(5.2)
1tec 
1tec 

39
The structural systems of equations under the imposed set of identifying cointegrating
restrictions with normalised cointegrating vector can be represented in the following
expanded matrix notation:
0,11 0,12 0,13 1 1
* *
0,21 0,22 0,23 2 3 1 2
0,31 0,32 0,33 1 3
0
0 1
D
W D
t t t
t t t
D Wp
t t t
p b b b p
e b b b e
p b b b p

  




       
                     
              
(5.3)
where,
0
r
B denotes the last row of the impact matrix 0B attributed to the transitory component.
The adjustment coefficient for the world price and the exchange rate is assumed to be
zero with the normalised coefficients for the cointegrating vector. Fisher and Huh
(2012) argued that once zero is imposed on both
W
p
 and e
 , the coefficients 0,13b
and 0,23b are also zero. This restriction implies that the world price and the exchange
rate do not contemporaneously respond to the structural import price shocks.
Furthermore, a Choleski decomposition can be used to exactly identify the three
variable system if the coefficient 0,12b is restricted to zero. The described restrictions
can be shown through the 0B matrix8
:
1 1
* *
2 3 1 2
1 3
1 0 0 0
1 0 0 1
1 D
W D
t t t
t t t
D Wp
t t t
p p
e e
p p

  




        
                     
                
(5.4)
where,  denotes unrestricted coefficient on the impact multiplier matrix.
8
The main diagonal is normalised to unity for construction of one standard deviation shock.

40
5.3 Main Results for First Stage Pass-through
The impulse response functions generated from the imposed Johansen normalised
coefficients with Choleski ordering are presented in Figure 5.2. This shows the
impulse responses of the world price, the exchange rate, and the import price to a one
standard deviation shock to each of the permanent and transitory shocks9
.
In the first panel, the world price responds most to the first permanent shock, while the
second permanent shock and the transitory shock have smaller effects on the world
price.
Figure 5.2: First Stage Impulse Response for One Standard Deviation Permanent
and Transitory Shocks With Johansen Normalised Coefficient: Full Sample
9
PW = World Price, TWI = Exchange Rate, PD = Import Price Over-the-docks.
-.04%
-.02%
.00%
.02%
.04%
.06%
2 4 6 8 10 12 14 16 18 20 22 24
P1 (PW) P2 (TWI) T1 (PD)
1. Response of World Price to
One S.D. Innovations
-.04%
-.02%
.00%
.02%
.04%
.06%
2 4 6 8 10 12 14 16 18 20 22 24
2. Response of Exchange Rate to
-.04%
-.02%
.00%
.02%
.04%
.06%
2 4 6 8 10 12 14 16 18 20 22 24
3. Response of Import Price Over-the-docks
to One S.D. Innovations

41
From the second panel, we see that a permanent shock (attributed to the world price)
has a persistent effect on the exchange rate. There is little response by either the world
price or the exchange rate to the transitory shock, per panel 1 and 2. The transitory
shock does have an initially large impact on the domestic import price shown in the
third panel. However, the effect does not persist in the long-run.
The third panel shows the response of import prices over-the-docks to the three shocks.
Both of the permanent shocks produce a decline in import prices. The second of the
permanent shocks, if interpreted as a positive exchange rate shock (an appreciation), is
associated with a fall in import prices. Hence, the second stage pass-through can be
built on the ground that near to full pass-through does occur in the first stage.
The forecast error variance decomposition is reported in Table 5.6. Firstly, 98% of the
forecast error variance for the world price can be attributed to the first permanent
shock. This pattern is maintained throughout the forecasted horizon. The remaining 2%
is attributed to the second permanent shock and the transitory shock. Secondly, in the
initial forecast horizon, most of the error variance of exchange rate is attributed to the
second permanent shock. However, as the forecast horizon rises, the importance of this
shock decreases and that of the first permanent shock increases. Lastly, as the forecast
horizon rises, more of the variance in the import price is due to the permanent shocks
than the transitory shock.

42
Table 5.6: First Stage Variance Decomposition of Permanent and Transitory
Shocks for Full Sample Period
Percentage of the variance in forecast error of W
tp due to:
Quarters
ahead
Permanent shock 1
1
W
p
t
Permanent shock 2
2
e
t
Transitory shock
3
D
p
t
1 100 0.00 0.00
2 98.55 1.30 0.14
3 98.00 1.90 0.10
4 98.22 1.85 0.06
8 98.26 1.72 0.02
12 98.29 1.70 0.01
16 98.31 1.68 0.01
20 98.32 1.68 0.01
Percentage of the variance in forecast error of te due to:
Quarters
ahead
Permanent shock 1
1
W
p
t
Permanent shock 2
2
e
t
Transitory shock
3
D
p
t
1 6.25 93.75 0.00
2 8.87 91.05 0.08
3 13.61 85.32 1.07
4 17.37 81.32 1.31
8 24.38 74.77 0.84
12 28.93 70.47 0.59
16 31.99 67.56 0.45
20 34.14 65.50 0.35
Percentage of the variance in forecast error of D
tp due to:
Quarters
ahead
Permanent shock 1
1
W
p
t
Permanent shock 2
2
e
t
Transitory shock
3
D
p
t
1 4.34 14.94 80.73
2 8.12 41.69 50.19
3 18.45 43.49 38.06
4 28.70 40.67 30.63
8 37.91 43.73 18.36
12 37.62 48.74 13.64
16 35.43 53.52 11.05
20 33.07 57.57 9.36

43
5.4 Australian Inflation Targeting Subsample
The results from the last section show the quick adjustment of the import price in
response to a highly persistent permanent shock to the exchange rate utilising the full
sample. However, the apparent high pass-through could be triggered by changes in
certain major policies. One of the major policy changes was the announcement of
inflation targeting by the RBA in mid 1993 with the intention to constrain inflation to
2-3% in the medium term. This implies that the central bank will actively utilise
monetary policy to combat medium term inflation. Thus, the stabilisation of the
exchange rate in response to a monetary policy shock would be affected, and hence,
Figure 5.3: First Stage Impulse Response for One Standard Deviation Permanent
and Transitory Shocks With DOLS Estimates: 1983Q2 - 1993Q1
-.04%
-.02%
.00%
.02%
.04%
.06%
2 4 6 8 10 12 14 16 18 20 22 24
-.04%
-.02%
.00%
.02%
.04%
.06%
2 4 6 8 10 12 14 16 18 20 22 24
-.04%
-.02%
.00%
.02%
.04%
.06%
2 4 6 8 10 12 14 16 18 20 22 24

44
pass-through to import price. Thus, two subsample tests involve the periods 1983Q2 to
1993Q1 just prior to the announcement of inflation targeting and post inflation
targeting period of 1993Q2 to 2010Q1.
Figure 5.3 presents the impulse response functions generated from the implied DOLS
estimates used for the cointegration equation in the periods of 1983Q2-1993Q110
.
From the third panel, the response of import price to a one standard deviation increase
in second permanent shock is approximately consistent when compared to the main
results reported. The shock persists with a long-run equilibrium of approximately
0.031%. The speed of adjustment is quick, thus consistent with the main result, where
import price reached its long-run equilibrium level after one year. Interestingly, the
import price reacts less in magnitude to the first permanent shock than in the main
results and the response of the import price levels off to zero in the long-run.
The impulse response functions shown in Figure 5.4 is generated from estimated
DOLS coefficients from the 1982Q2 to 1993Q1 subsample. This is due to the incorrect
sign on the world price shown in Table A5.3 in Appendix 5 for all three methods. In
the third panel, the magnitude of the response of the import price to the second
permanent shock is consistent with the results from subsample prior inflation targeting.
However, the speed of pass-through is much slower than the prior inflation targeting
subsample. Thus, we can draw from the two subsamples that inflation targeting
reduces the speed of exchange rate pass-through to import price, although the
magnitude is broadly consistent across the long-run forecast horizon.
10
Implied DOLS estimates are used instead of the Johansen normalised coefficients due to the two pass-
through coefficients been above 1. Both sets of estimates are reported in Appendix 5 (Table A5.1).

45
From the forecast error variance decomposition in Table 5.7, the same conclusion can
be drawn compared to the corresponding forecast error variance in Table 5.6. The
importance of the second permanent shock increased substantially, which is reflected
Figure 5.4: First Stage Impulse Responses for One Standard Deviation
Permanent and Transitory Shocks With DOLS Estimates: 1993Q2 - 2010Q1
in the first period, where half of the variance in import price is attributed to the second
permanent shock. There seems to be a high and persistent pass-through of the
exchange rate to the import price prior to inflation targeting. Overall, the pass-through
of a permanent shock derived from innovations of the exchange rate to the import
price is complete and significant.
-.06%
-.04%
-.02%
.00%
.02%
.04%
.06%
2 4 6 8 10 12 14 16 18 20 22 24
-.06%
-.04%
-.02%
.00%
.02%
.04%
.06%
2 4 6 8 10 12 14 16 18 20 22 24
-.06%
-.04%
-.02%
.00%
.02%
.04%
.06%
2 4 6 8 10 12 14 16 18 20 22 24

46
Shocks for Subsample 1983Q2-1993Q1
tp due to:
Quarters
ahead
Permanent shock 1
1
W
p
t
Permanent shock 2
2
e
t
Transitory shock
3
D
p
t
1 100 0.00 0.00
2 99.25 0.67 0.08
3 98.30 1.66 0.04
4 97.10 2.86 0.04
8 95.37 4.58 0.05
12 94.61 5.34 0.05
16 94.19 5.77 0.04
20 93.93 6.04 0.03
Quarters
ahead
Permanent shock 1
1
W
p
t
Permanent shock 2
2
e
t
Transitory shock
3
D
p
t
1 4.34 95.66 0.00
2 8.11 91.89 0.00
3 16.98 82.78 0.24
4 20.76 79.04 0.20
8 30.12 69.68 0.19
12 33.03 66.80 0.17
16 33.88 65.98 0.14
20 34.03 65.86 0.11
tp due to:
Quarters
ahead
Permanent shock 1
1
W
p
t
Permanent shock 2
2
e
t
Transitory shock
3
D
p
t
1 1.35 50.81 47.84
2 0.40 82.06 17.55
3 5.37 77.39 17.24
4 10.35 74.33 15.32
8 15.60 75.09 9.31
12 14.30 78.75 6.95
16 11.93 82.50 5.56
20 9.88 85.49 4.62

47
Shocks for Subsample 1993Q2-2010Q1
tp due to:
Quarters
ahead
Permanent shock 1
1
W
p
t
Permanent shock 2
2
e
t
Transitory shock
3
D
p
t
1 100 0.00 0.00
2 97.31 2.39 0.30
3 96.84 3.01 0.16
4 97.27 2.59 0.14
8 98.21 1.72 0.07
12 98.44 1.51 0.05
16 98.54 1.42 0.04
20 98.60 1.37 0.03
Quarters
ahead
Permanent shock 1
1
W
p
t
Permanent shock 2
2
e
t
Transitory shock
3
D
p
t
1 6.28 93.72 0.00
2 7.40 92.50 0.10
3 9.35 88.55 2.09
4 10.57 86.53 2.90
8 12.12 85.85 2.03
12 15.76 82.73 1.51
16 19.60 79.25 1.15
20 22.97 76.12 0.91
tp due to:
Quarters
ahead
Permanent shock 1
1
W
p
t
Permanent shock 2
2
e
t
Transitory shock
3
D
p
t
1 3.56 15.10 81.34
2 10.18 46.64 43.18
3 23.24 48.87 27.89
4 33.33 45.21 21.46
8 40.33 45.45 14.22
12 38.82 49.72 11.46
16 35.46 54.61 9.93
20 32.06 59.07 8.87

48
Lastly, from the forecast error decompositions presented in Table 5.8, compared to the
previous subsample, the importance of the second permanent shock is diminished to
the variance of import price, while the transitory shock becomes more dominant in the
initial forecast horizons. Thus, inflation targeting contributes to the increased
resilience of the exchange rate pass-through, even in the first stage.
5.5 Rolling Window for Coefficient Stability
To perform a stability check on the long-run coefficients implied by the Johansen
normalised coefficients and DOLS, recursive estimation of Johansen and DOLS
methodology with a window of 20 on the full sample period from 1983Q2 to 2010Q1
is implemented. Figure 5.5 reports the recursive rolling window on DOLS estimation
for the first stage pass-through coefficients of the exchange rate and the world price11
.
Figure 5.5: First Stage Pass-through Rolling Window on DOLS Coefficients
From Figure 5.5, the coefficients on both the exchange rate and the world price appear
to be stable with fluctuations between 1.08 to 1.28 for the exchange rate and 0.6 to 1.5
for the world price. Additionally, the 2 standard error bands appear larger with
increasing sample periods, which reflect increased uncertainty in periods after 1993.
11
Rolling window estimates using the Johansen methodology are reported in Appendix 7.
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
88 90 92 94 96 98 00 02 04 06 08
TWI TWI LB TWI UB
0.0
0.4
0.8
1.2
1.6
2.0
88 90 92 94 96 98 00 02 04 06 08
PW PW LB PW UB

49
6. Second Stage Pass-through: Import Price to Inflation
This chapter describes how much of the first stage exchange rate fluctuations pass-
through from the after-tax import prices to the domestic retail price. Thus, the
dynamics of the import price to inflation will be assessed. I will address the role
played by the mark-up on cost to help explain the adjustment of prices to fluctuations
in the exchange rate. The chapter concludes with the inflation targeting subsample
used to determine whether the announcement of inflation targeting triggers any
differences in variations of mark-up on cost. In addition, stability tests are conducted
on the long-run coefficients.
6.1 Data Description and Properties
Second stage pass-through variables include: the retail price of consumption goods
( )D
tr , the after-tax import price over-the-docks ( )T
tp , and the costs borne by retailers
and importers ( )C
tp . All data obtained are in quarterly frequency with the same sample
periods consistent with the first stage analysis.
The retail price imported consumption goods is constructed using five consumption
subgroups or expenditure classes sourced from the ABS all group CPI. This proxy
entails the need to re-weight each subgroup or expenditure class by their respective
historical weights. These weights are also sourced from the ABS. Thus, all five
weighted subgroups or expenditure classes are aggregated to form a proxy for the
retail price of consumption goods.
In comparison to the approach used by Dwyer and Lam (1994), they deducted
weighted sub-groups or expenditure classes from the total CPI that were not
predominately imported consumption goods. The rest were aggregated to form a proxy

50
for retail price of consumption. This approach is not followed due to several issues that
arose with the sampled data. The main issue is due to missing observations in earlier
quarters and re-categorisation of subgroups or expenditure classes of the sample data1
.
The after-tax import price is simply the tariff-adjusted import price over-the-docks
from the first stage. This series is defined as import price times the index of (1 +
average tariff). Average tariff is defined as taxes on international trade in proportion to
the value of merchandise imports, consistent with Dwyer and Lam (1994).
Five major costs are distinguished, namely: international freight cost, taxes on
international trade, unit labour costs, domestic transport cost, and other expenses.
However, the series excludes the tax on international trade due to its inclusion in after-
Figure 6.1: Second Stage Pass-through Variables
1
Please refer to Appendix 1 for a full treatment of data issues.
3.8
4.0
4.2
4.4
4.6
4.8
5.0
84 86 88 90 92 94 96 98 00 02 04 06 08
Retail Price of Consumption Goods
3.8
4.0
4.2
4.4
4.6
4.8
5.0
84 86 88 90 92 94 96 98 00 02 04 06 08
After-tax Import Price
3.8
4.0
4.2
4.4
4.6
4.8
5.0
84 86 88 90 92 94 96 98 00 02 04 06 08
Input Costs Borne by Retailers and Importers

51
tax import price. All series are obtained from the ABS website with the exception of
domestic freight. Domestic freight is sourced from the Australian Bureau of
Agricultural and Resource Economics (ABARE). However, the data sample ends in
Q1 2010. The construction of these costs is consistent to that of Dwyer and Lam (1994)
with only minor change to other expenses.
Figure 6.1 shows graphical representations of the second stage pass-through variables.
We can infer from the plots that all series contains non-zero intercept with linear trend,
although, linear trend in import price is debateable.
The ADF test presented in Table 6.1 shows all variables contain a unit root and their
levels are I(1)2
. The EG test presented in Table 6.2 shows no cointegration amongst
any group of variables. However, from the Johansen test presented in Table 6.3, the
trace statistics show one cointegration relationship amongst the variables, while the
maximum eigenvalue statistics shows no cointegration relationship at 5% level.
Although, only trace statistics show one cointegration relationship, the specification of
including the deterministic trend is to be used as a sensitivity check for robustness of
the main result in second stage pass-through3
.
The normalised cointegrating coefficient shown in Table 6.4 suggests the long-run
elasticity between retail price and after-tax import price is positive with a magnitude of
above 3. In comparison to Dwyer and Lam (1994), their long-run elasticity of 0.66 is
much lower than the estimate obtained from the Johansen methodology. The OLS and
DOLS estimate for import price is relatively similar to the estimated coefficients in
2
Although, retail price seems to be marginally I(1) shown in the ADF test, however, theory suggest that
retail price is I(1), thus, retail price is treated as I(1).
3
Impulse response functions are generated for both first and second stage pass-through with the
inclusion of trend specifications reported in Appendix 6.

52
Table 6.1: Second Stage Augmented Dickey-Fuller Test Results
Variables
Level First Differences
Lags
Test
Stats
CV at
5%
P-
value
Lags
Test
Stats
CV at
5%
P-
value
D
tr 2 -3.28 -3.45 0.07 1 -2.40 -2.89 0.14
T
tp 6 -2.94 -3.45 0.15 1 -8.61 -2.89 0.00
C
tp 0 -2.58 -3.45 0.29 0 -11.05 -2.89 0.00
Lag length selected based on t-statistics with maximum lag length of 12.
Constant and linear trend included in the levels of all three variables.
P-value is based on MacKinnon (1996) one sided P-value.
Table 6.2: Second Stage Pass-through Engle-Granger Test Results
Dependent Variable Lags t-statistics P-value
D
tr 4 -2.34 0.57
T
tp 4 -2.37 0.56
C
tp 4 -1.98 0.74
Selection of lags based on t-statistics with 5 maximum included lags.
Null hypothesis: No cointegration
Table 6.3: Second Stage Pass-through Johansen Cointegration Test Results
Level VAR (3)
Trace Maximum Eigenvalue
Rank Test
statistics
5% CV P-value
Test
statistics
5% CV P-value
0 41.29* 35.19 0.01 21.76 22.30 0.06
1 19.53 20.26 0.06 12.97 15.89 0.14
2 6.56 9.16 0.15 6.56 9.16 0.15
*Statistically significant at 5% level.
Intercept in CE with no deterministic trend included in both CE and VAR4
.
Table 6.4: Second Stage Pass-through Estimation Results
Variables
Normalised CI
Coefficients
OLS DOLS
T
p
3.05
(0.82)
0.44
0.43
(0.15)
0.01
C
p
2.43
(0.73)
0.75
0.71
(0.04)
0.00
Constant included in regression but omitted from report.
DOLS using AIC (Max lag length 5) lag and lead both equal to three with HAC standard errors (fixed
4 lags).
The reported normalised coefficients are in the form of 1 1 1 2 1
D T C
t t tr p p     .
4
Deterministic trend is excluded from estimation results since it is statistically insignificant at 5% level.

53
Dwyer and Lam (1994). The DOLS estimates are used to form the error correction
term in the following sections.
6.2 Model Estimation and Identification
From Table 6.5, the estimation results from a VECM(2) specification shows the
adjustment coefficients on the error correction term is statistically insignificant at the 5%
level for both growth in retail price and after-tax import price5
. However, the growth
Table 6.5: Adjustment Coefficients on Error Correction of VECM(2)
Dependent
Variable
Independent
Variables
Coefficient t-statistics
D
tr 1tec 
-0.01
(0.01)
-1.21
T
tp
0.12
(0.10)
1.15
C
tp
0.17
(0.05)
3.51**
* Statistically significant at 5%
** Statistically significant at 1%
Error correction term is defined as 1 1 1 10.43 0.71 0.59D T C
t t t tec r p p       using DOLS estimated
coefficients from Table 6.4.
of total costs inputs shows a statistically significant result even at 1%. Hence, given
the current specifications, we can draw from the result that the transitory shock is
derived from the input costs and the two permanent shocks derives from the retail
price and the import price.
The identification strategy under weak exogeneity seen in the first stage is also
implemented for the second stage. Identifying structural cointegrating restrictions
analogous to the first stage involve the following:
5
VECM(2) is constructed using 2 lags of differenced endogenous variables with intercept but no trend
in cointegrating vector.
1tec 
1tec 

54
0,11 0,12 0,13
0,21 0,22 0,23
0
0
T
D
C
p
r
p
b b b
b b b



 
    
     
    
  
(6.1)
Consequently, the result of the structural system under the imposed identifying
structural restrictions with the equivalent Choleski decomposition for the second stage
becomes:
1 1
* *
2 3 1 2
1 3
1 0 0 0
1 0 0 1
1 C
T D
t t t
D T
t t t
C Cp
t t t
p r
r p
p p

  




        
                     
                
(6.2)
6.3 Main Results for Second Stage Pass-through
Figure 6.2 presents the response of each of the variables to a one standard deviation
increase in each of the permanent and transitory shocks6
. The second panel shows two
important results. Firstly, a one standard deviation permanent shock thought to be
attributed to import price increased retail price by 0.018% in the long-run equilibrium.
Specifically, compared to the first stage pass-through, the speed of pass-through is
very slow; by the end of one year, only 0.002% of the first shock is transmitted to the
domestic retail price. Additionally, the magnitude over the long-run equilibrium is
approximately 0.018%, which is almost half as low as 0.033% for the long-run pass-
through of the exchange rate to import price in the first stage.
Secondly, the role of the mark-up in retail prices can be seen from the transitory shock,
as it appears that there is no impact of a raise in mark-up on the retail price7
.
6
PT = After-tax Import Price, RPI = Retail Price of Imported Consumption Goods, PC = Input Costs
Borne By Retailers and Importers.
7
The cause of the result is due to the assumption that both after-tax import price and retail price does
not adjust contemporaneously with input cost. As a robustness check, zero restrictions will be imposed
on the adjustment coefficients for import price and input costs to compare with the main results.

55
Furthermore, the role of mark-up in relation to import price can also be drawn from
the first panel. The response of the after-tax import price declined after the transitory
Figure 6.2: Impulse Responses for One Standard Deviation Permanent and
Transitory Shocks With DOLS Estimates: Full Sample
shock attributed to the mark-up. However, due to the nature of transitory shock, the
response of import price drives back to zero long-run equilibrium. By combining this
result with previous findings, an unfavourable shock in exchange rate raises import
price, thus lowers mark-up (shown in panel 1), and increases the retail price (shown in
panel 2)8
.
8
Consequently, a favourable exchange rate shock reduces the import price due to retailers response of
reducing their mark-up on costs and reduces retail price.
-.04%
-.02%
.00%
.02%
.04%
.06%
.08%
10 20 30 40 50 60 70 80 90 100
P1 (PT) P2 (RPI) T1 (PC)
1. Response of After-tax Import Price to
-.04%
-.02%
.00%
.02%
.04%
.06%
.08%
10 20 30 40 50 60 70 80 90 100
2. Response of Retail Price to
-.04%
-.02%
.00%
.02%
.04%
.06%
.08%
10 20 30 40 50 60 70 80 90 100
3. Response of Input Costs to

56
One final result can be drawn from the last panel that a one standard deviation
permanent shock attributed to the retail price raises the domestic input costs in the
long-run. The magnitude is large and significant. In addition, the first permanent shock
reduces the total input costs. This means that domestic shock has an important
influence on the input cost decisions faced by domestic retailers.
From the forecast error variance decomposition shown in Table 6.6, initially, most of
the error variance in growth of retail price is attributed to the second permanent shock.
However, the first permanent shock attributed to the after-tax import price slowly
increased confirming the speed of pass-through is slow. Interestingly, the last section
in Table 6.6 shows the increasing role of the first permanent shock quickly dominated
the forecast error variance of growth in input costs after one year period. This is
consistent with the claim made before that domestic import price shock has an
important role in input costs decisions faced by retailers.

57
Table 6.6: Variance Decomposition of Permanent and Transitory Shocks: Full
Sample Period
Percentage of the variance in forecast error of T
tp due to:
Quarters
ahead
Permanent shock 1
1
T
p
t
Permanent shock 2
2
D
r
t
Transitory shock
3
C
p
t
1 100 0.00 0.00
2 99.17 0.51 0.32
3 97.50 1.25 1.26
4 96.26 1.99 1.75
8 94.65 3.86 1.49
12 93.39 5.42 1.20
16 92.49 6.55 0.96
20 91.89 7.33 0.78
tr due to:
Quarters
ahead
Permanent shock 1
1
T
p
t
Permanent shock 2
2
D
r
t
Transitory shock
3
C
p
t
1 1.17 98.83 0.00
2 0.49 99.50 0.01
3 1.10 98.89 0.00
4 1.88 98.10 0.01
8 3.30 96.63 0.07
12 4.22 95.70 0.08
16 4.90 95.03 0.08
20 5.45 94.48 0.07
Percentage of the variance in forecast error of C
tp due to:
Quarters
ahead
Permanent shock 1
1
T
p
t
Permanent shock 2
2
D
r
t
Transitory shock
3
C
p
t
1 2.60 2.08 95.31
2 2.19 1.62 96.19
3 4.87 1.22 93.91
4 7.36 1.18 91.45
8 21.21 7.03 71.77
12 32.80 19.17 48.02
16 37.45 31.70 30.85
20 37.66 42.09 20.25

58
6.5 Australian Inflation Targeting Subsample
The objective of the subsample estimation is to address any differences in the role of
mark-up in periods before and after the inflation targeting announcement. Additionally,
this serves to provide a robustness check on the main results obtained previously. The
impulse response functions are derived from the same model specifications with
cointegration equation formed from estimates implied by the DOLS are reported and
compared against previous results for both subsample prior and post 1993 Q29
.
Transitory Shocks With DOLS Estimates: 1983Q2 - 1993Q1
9
Impulse response functions and forecast error variance decompositions with implied estimates
obtained from Johansen normalised coefficients are reported in Appendix 5 used as a comparison to the
results obtained here.
-.02%
.00%
.02%
.04%
.06%
.08%
10 20 30 40 50 60 70 80 90 100
-.02%
.00%
.02%
.04%
.06%
.08%
10 20 30 40 50 60 70 80 90 100
-.02%
.00%
.02%
.04%
.06%
.08%
10 20 30 40 50 60 70 80 90 100

59
Figure 6.3 shows the impulse response functions generated using the implied
estimated cointegrating coefficients from the DOLS regression. The second panel
shows the long-run equilibrium of a one standard deviation permanent shock attributed
to import price raises the response of retail price by approximately 0.04%. Compared
to the previous second panel in the main result, the magnitude is twice as large and the
speed of pass-through is slightly faster. The role of the mark-up in the first panel is
slightly diminished compared to the main results. However, since input costs generate
transitory shock, retail price is still unaffected by change in the mark-up. Additionally,
the response of all included variables are persistent to the second permanent shock
derived from retail price. Overall, the impulse response functions are robust to the
main results obtained previously.
Figure 6.4 shows impulse response functions during post inflation targeting
announcement derived from the implied DOLS estimates10
. Compared to Figure 6.3,
with the exact same specification using imposed DOLS estimates for periods prior to
inflation targeting, and the pass-through here is negligible in magnitude with
approximately the same speed. The only difference is due to the change in the
persistence of the shock; input cost is attributed to transitory shock prior inflation
targeting, while retail price becomes transitory post inflation targeting. Thus, we can
draw from the two subsamples that the effect of inflation targeting caused the decline
in persistency of shock that attributed to retail price and raised the persistency of input
costs.
Furthermore, the third panel shows that the transitory shock attributed to the mark-up
raises the long-run equilibrium of retail price by approximately 0.012%. Comparing to
10
Implied DOLS estimates were used to substitute the Johansen normalised coefficients since the
coefficient on import price had incorrect sign.

60
the previous prior inflation target sample, the role of mark-up becomes important after
the announcement and implementation of inflation targeting by RBA. Thus, the
reduction in persistency of retail price with a more fluctuating and persistent mark-up
on cost implies reduction in pricing power of retailers consistent with Taylor (2000).
In summary, the portion of the permanent shock derived from exchange rate change is
absorbed by variation in mark-up after the implementation of inflation targeting,
although, mark-up plays a limited role prior inflation target periods.
Transitory Shocks With DOLS Estimates: 1993Q2 - 2010Q1
-.02%
.00%
.02%
.04%
.06%
.08%
10 20 30 40 50 60 70 80 90 100
P1 (PT) P2 (PC) T1 (RPI)
-.02%
.00%
.02%
.04%
.06%
.08%
10 20 30 40 50 60 70 80 90 100
-.02%
.00%
.02%
.04%
.06%
.08%
10 20 30 40 50 60 70 80 90 100

61
Table 6.7: Second Stage Variance Decomposition of Permanent and Transitory
Shocks Implied By DOLS Estimates: 1983Q2-1993Q1
tp due to:
Quarters
ahead
Permanent shock 1
1
T
p
t
Permanent shock 2
2
D
r
t
Transitory shock
3
C
p
t
1 100 0.00 0.00
2 97.08 1.95 0.97
3 96.60 2.52 0.87
4 96.30 2.91 0.79
8 95.28 4.31 0.41
12 94.07 5.68 0.25
16 92.86 6.97 0.17
20 91.75 8.12 0.12
tr due to:
Quarters
ahead
Permanent shock 1
1
T
p
t
Permanent shock 2
2
D
r
t
Transitory shock
3
C
p
t
1 8.85 91.15 0.00
2 15.97 82.98 1.05
3 20.59 78.99 0.41
4 24.26 75.51 0.23
8 30.11 69.85 0.04
12 32.87 67.11 0.02
16 34.50 65.49 0.01
20 35.56 64.43 0.01
tp due to:
Quarters
ahead
Permanent shock 1
1
T
p
t
Permanent shock 2
2
D
r
t
Transitory shock
3
C
p
t
1 15.88 2.43 81.69
2 20.74 1.64 77.62
3 27.00 2.40 70.60
4 32.43 4.63 62.94
8 35.95 31.42 32.64
12 27.54 54.57 17.89
16 19.75 69.07 11.18
20 14.29 78.02 7.68

62
In comparison of the forecast error decompositions between two of the subsamples
shown in Table 6.7 and Table 6.8, two results are drawn. The subsample prior to
inflation target shows the first permanent shock (import price) has a higher
contribution to the variance in retail price compared to the full sample. However, the
role of the same shock is much lower in the post inflation targeting announcement
period shown in last section of Table 6.8. Consequently, the role of the mark-up
becomes significant in the variance of retail price post inflation target period.
A further result lies in the differences of the first permanent shock to the variance of
input costs between the two subsamples. After the implementation of inflation
targeting, the first permanent shock plays no role in explaining the input costs. This
implies that the result obtained from the full sample forecast error decompositions is
driven by the significance of the role in import price shock prior to the inflation
targeting.

63
Table 6.8: Second Stage Variance Decomposition of Permanent and Transitory
Shocks Implied By DOLS Estimates: 1993Q2-2010Q1
tp due to:
Quarters
ahead
Permanent shock 1
1
T
p
t
Permanent shock 2
2
C
p
t
Transitory shock
3
D
r
t
1 100 0.00 0.00
2 99.80 0.01 0.19
3 98.70 0.89 0.40
4 97.53 2.03 0.44
8 97.97 1.71 0.33
12 98.32 1.43 0.25
16 98.60 1.20 0.20
20 98.81 1.02 0.17
tp due to:
Quarters
ahead
Permanent shock 1
1
T
p
t
Permanent shock 2
2
C
p
t
Transitory shock
3
D
r
t
1 1.67 98.33 0.00
2 0.89 97.42 1.69
3 0.82 96.09 3.09
4 0.67 95.45 3.88
8 0.48 95.29 4.23
12 0.41 96.29 3.30
16 0.38 97.14 2.47
20 0.37 97.73 1.89
tr due to:
Quarters
ahead
Permanent shock 1
1
T
p
t
Permanent shock 2
2
C
p
t
Transitory shock
3
D
r
t
1 6.18 1.13 92.69
2 3.33 1.93 94.75
3 2.08 1.23 96.69
4 2.16 1.02 96.82
8 2.71 12.88 84.41
12 3.27 38.86 57.86
16 3.31 59.14 37.16
20 3.18 71.94 24.88

64
6.6 Rolling Window for Coefficients Stability
Similar to the rolling window performed on first stage pass-through results, recursive
estimation with window of 20 was implemented on both Johansen and DOLS
methodology. Figure 6.5 shows the coefficient on both after-tax import prices are
stable with fluctuation ranging from 0.48 to 0.70. Coefficients on input costs shows
greater fluctuations in the periods prior to 1990, which stabilises between 0.50 to 0.80.
Figure 6.5: Second Stage Pass-through Rolling Window on DOLS Coefficients
.2
.3
.4
.5
.6
.7
.8
.9
88 90 92 94 96 98 00 02 04 06 08
PT PT LB PT UB
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
88 90 92 94 96 98 00 02 04 06 08
PC PC LB PC UB

65
7. Combined Stage Pass-through: Exchange Rate to Retail Price
The motivation of this chapter is two-fold. The first is to trace out the direct impact of
the exchange rate changes on domestic retail prices. More importantly, the
decomposition of import prices into the exchange rate and the world price allow
further insight on how mark-up varies with the exchange rate additional to the
response of retail price.
7.1 Data Properties
This section shows the evidence towards the removal of the tariff component on the
after-tax import price when the index of average tariff enters the system as a separate
variable, in accordance with the theoretical framework. Table 7.1 shows the estimated
coefficients from OLS, DOLS, and normalised cointegrating coefficients derived from
the Johansen methodology. The estimates from both the OLS and DOLS both show
the index of average tariff is statistically insignificant at the 5% level. Although, the
long-run elasticity implied by Johansen is statistically significant at 5%, however, it is
insignificant at 1% level.
Table 7.1: Combined Stage Pass-through Estimation Results
Variables
Normalised CI
Coefficients
OLS DOLS
tT 2.79
(1.35)
-1.14
0.03
(0.66)
0.97
te -0.56
(0.09)
-0.56
-0.39
(0.07)
0.00
W
tp
0.55
(0.21)
0.34
0.13
(0.13)
0.32
C
tp
-0.62
(0.16)
0.40
0.81
(0.08)
0.00
Constant and deterministic trend included in all three estimates but omitted from report.
DOLS using AIC (Max lag length 5) lag and lead both equal to five with HAC standard errors (fixed 1
lag).
The reported long-run elasticity are in the form of 1 1 2 1 3 1
D T C
t t t tr T p p      

YU_Pass-through_of_exchange_rate_shocks

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