OBSERVARE
Universidade Autónoma de Lisboa
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EXCHANGE RATE PASS-THROUGH TO PRICE INDICES IN IRAN
MOHSEN MOHAMMADI KHYAREH
m.mohamadi@ut.ac.ir
Assistant professor, University of Gonbad Kavous (Iran)
Abstract
One of the major challenges for monetary policy is to predict how exchange rate fluctuations
affect inflation and price indices. Hence, the main objective of this study is to examine the
exchange rate fluctuation in the price indices in Iran. This paper analyzes the effects of
exchange rate fluctuations on price indices and other macroeconomic variables of Iran during
the period of 2004-Q1 to 2018-Q4, using the framework of a recursive VAR model, drawing
on Bernanke (1986) and Sims (1986).The results indicate that the transfer of exchange rate
changes to price indices is imperfect, such that the exchange rate path through to consumer,
producer, and import prices is from 14.68%, 15.55% and 18.22% in the first period increase
to 51.78%, 53.15% and 88.14% in the 13th period. In addition, the results indicate that the
exchange rate path-through decreases along the distribution chain, with the highest exchange
rate passing through the import prices, producer prices and consumer prices, respectively.
The result has interesting implications for Iran’s ability to attain an effective inflation-targeting
regime. Monetary policy makers should curb exchange rate fluctuations by adopting
appropriate exchange rate policies in order to minimize the uncertainty of the consumer price
index. The study contributes to the literature by assessing the effect of changes in the
exchange rate (the Iranian Rial vis-à-vis the US$) on prices using an updated time series from
2004 to 2018. It addresses the limitations of the previous studies, which found no strong
relationship between the exchange rate and inflation rate in the Iranian context. One of these
limitations was using the CPI, as the only price index.
Keywords
Exchange rate path-through, Monetary Policy, price index, Recursive VAR, Iran
How to cite this article
Khyareh, Mohsen Mohammadi (2021). Exchange rate pass-through to price índices in Iran.
Janus.net, e-journal of international relations. Vol12, Nº. 1, May-October 2021. Consulted
[online] at date of last visit, https://doi.org/10.26619/1647-7251.12.1.8
Article received on October 31, 2020 and accepted for publication on March 4, 2021
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Exchange rate pass-through to price indices in Iran
Mohsen Mohammadi Khyareh
142
EXCHANGE RATE PASS-THROUGH TO PRICE INDICES IN IRAN
MOHSEN MOHAMMADI KHYAREH
Introduction
Due to foreign exchange transactions in petroleum and petrochemical products, Iran has
always had a positive trade balance. However, in the last few months of 2017 and since
the beginning of 2018, the country’s foreign exchange market has experienced
substantial volatility, and the open market exchange rate has risen. The reasons for the
exchange rate increase should be found from two types of background factors and
aggravating factors. In the past few years, the most important potential factors have
emerged that have made the country vulnerable to the foreign exchange system,
including the rapid increase in liquidity restrictions on Iranian banks related to the
transfer of funds; dependence on intermediate currencies such as the US dollar and euro,
and dependence on centralized SWIFT. However, the factors leading to the sharp rise in
the exchange rate are the sharp rise in the level of uncertain capital outflows in the
country's economic environment, withdrawal from brokerage houses, and new
restrictions on the country's foreign exchange trading routes.
It should be noted that not all factors affecting exchange rate fluctuations are influenced
by purely economic factors, and many non-economic factors, such as political
developments, can affect the exchange rate expectations by influencing society's
expectations. However, to consider the coincidence of political developments and
exchange rate fluctuations during the period 2013-2018, we should say that the election
of US president Donald Trump in November 2016 caused an increase in the fluctuation
of the exchange rate in Iran, and, moreover, Trump threat to withdraw from the joint
comprehensive action plan (JCPOA)
1
caused further fluctuations in the Iranian exchange
rate. Thus, in order to keep inflation low and stable, it is necessary to identify the
important factors involved in inflation in Iran. Meanwhile, part of the high inflation in Iran
is due to foreign price shocks due to the high share of imported goods in GDP, so, the
high exchange rate fluctuations have prompted our attention to study the exchange rate
on price indices in Iran. As exchange rate is one determinant of inflation, changes in
exchange rates are considered important in the design of monetary policy, especially
when a country has a flexible exchange rate policy as well as an open trade policy. It has
1
The Joint Comprehensive Plan of Action (JCPOA( known commonly as the Iran nuclear deal or Iran deal, is
an agreement on the Iranian nuclear program reached in Vienna on 14 July 2015, between Iran and the
P5+1 (the five permanent members of the United Nations Security Council—China, France, Russia, United
Kingdom, United States—plus Germany ( together with the European Union.
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thus been an ongoing challenge for economists to examine the Exchange rate pass-
through (ERPT) to domestic prices. Therefore, the main purpose of the present study is
to evaluate the degree of ERPT in Iran. To this end, the dynamic response of inflation to
price indices to the exchange rate shocks in Iran will be examined.
According to Figure 1, the development of the country’s foreign exchange market shows
the turbulence of the market and the dramatic volatility of the past few decades. At the
same time that Iran implemented a unified exchange rate policy in 1993, the exchange
rate rose sharply due to the imbalance of international payments, mainly due to the fall
in oil prices and the issue of debt repayment due. Later, in 2002, through the financial
support of the central bank’s foreign exchange reserves and the central bank’s financial
coverage, a unified exchange rate policy was implemented, which greatly reduced the
distance between the free market and the official exchange rate. Thus providing relative
stability in the foreign exchange market. In addition, between 2002 and mid-2010, a
managed floating system is being implemented. However, since the country’s main
supplier depends on the foreign exchange income from oil exports, fluctuations in world
oil prices and the decision to use oil income in the annual budget have led to the
fragmentation of the foreign exchange market. Since mid-2010, after the gap between
the market and the official exchange rate widened, the exchange market fluctuated
sharply in 2011 and 2012.
Figure 1: Trend of annual percentage change in variables
Source: Time series database, Central Bank of Iran
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One of the most important reasons for the increase in the exchange rate is the growing
speculative demand for currencies, the expected increase in foreign exchange rate
returns and the expected increase in the expected profits of exchange rate purchases.
The continued rise of market exchange rates, negative future prospects for sanctions,
the country’s exchange rate income and foreign exchange reserves, the expansion of
lease space, and the increase in inflationary pressures caused the country to be on the
verge of exchange rate crisis in late 2011 and mid-2018. On the other hand, the growth
rate of liquidity and oil revenue in the past few years, and more importantly, the quality
of its distribution, is another important factor in the continued exchange rate fluctuations.
Speculative demand channel is a channel that affects liquidity and exchange rate
volatility. It should also be noted that the accumulation of harmful liquidity over the years
has promoted economic turmoil in various fields, such as the land, housing market, gold
market and, recently, the foreign exchange market. In addition, factors such as high
inflation in the past few years, lack of exchange rate adjustment, and high dependence
on oil revenue have become factors affecting exchange rate fluctuations in recent years.
However, sanctions are the most influential factor when currencies have recently begun
to fluctuate. Therefore, due to the close relationship between exchange rate fluctuations,
liquidity and oil revenue, this article studies the relationship between them.
In the case of Iran, the relationship between price index and ERPT has been tested using
VAR and structural variance (SVAR) models. The salient feature of this study is the use
of a recursive VAR model to check ERPT. This study investigated the effects of oil revenue,
economic growth, money supply growth, and exchange rate fluctuations. So far, the
effects of these variables have not been studied under the recursive VAR model. Another
feature of the model is the ability to evaluate the sustainability of the results by
evaluating the sensitivity of the results to different Cholesky rankings. Compared with
other models used in previous researches, another feature of this model is that it can
examine the impact of different monetary authorities' policies on different economic
shocks.
In the second part of this article, we review Iran’s exchange rate policy, and in the third
part we review the theoretical and empirical literature. The fourth part discusses research
methods and data. The fifth part discusses the experimental results, and finally the
conclusions in the sixth part are discussed.
1. Overview of the Iranian Financial System
Over the past decades, the mechanism for determining foreign exchange policies and
exchange rates has changed widely and has generally shifted over time to more flexible
arrangements. After the adoption of the fixed exchange rate system during the years
(1959-1978), the multiple exchange rate system was applied in the years following the
Islamic revolution and until 1992. This system has been in place since (1994-2001) and
since mid-2010 till now. After that, the managed floating system has been implemented
in the country with two completely different experiences. In the first experience in 1993,
due to the imbalance in the balance of payments, which was mainly due to the fall in oil
prices and the repayment of overdue debts, the system failed. However, the managed
floating system again implemented in 2002, and due to high profits and abundance of
foreign exchange rate earnings continued until mid-2010.
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The event that affected the operation of the managed float system was the imposition of
sanctions against the country's financial system in October 2010. Although the history of
Western hostility towards Iran and its manifestation in the form of unilateral and
multilateral sanctions dates back to the early formation of the Islamic Republic of Iran;
by the middle of 2010, western sanctions against Iran have taken a different course, in
terms of quantity and scope. It was broader in terms of enforcement rules and
mechanisms than in previous sanctions.
In the new series of western sanctions on Iran, adopted in July 2010 for the first time in
addition to the United States, Europe has also imposed sanctions on Iran and has
imposed sanctions on Iranian financial institutions, central banks, insurance companies,
oil and gas exports, petrochemicals and products. Oil and financial transactions (such as
SWIFT) and the transfer of foreign exchange earnings were also included in the sanctions.
One of the most important and immediate consequences of the imposition and imposition
of sanctions was its impact on the country's exchange rate system and foreign exchange
market. Evidence has been that the foreign exchange market has reacted to the sanctions
in the near-term over the past six years. In the real sector of the economy, sanctions
have also reduced foreign exchange earnings and reduced exchange rate supply by
restricting exports of oil, gas, petroleum products and petrochemicals.
1.1. Developments and history of exchange rate arrangements in Iran
Investigating the developments of Iran's exchange rate arrangements since 1957 has
shown a shift from fixed exchange rate system to more flexible exchange rate system.
In general, the Iranian economy during this period, it has experienced three types of
exchange rate policy adopted over six different times.
Prior to the victory of the Islamic Revolution, the country had a stable exchange rate
system. However, government oversight, exchange rate rationing, and setting priorities
for foreign exchange spending continued until 1973. In 1974, the price of oil on the world
markets was remarkably high increased. With the increase in foreign exchange earnings
from oil exports, the quota was eliminated (by maintaining a stable exchange rate
system). After the victory of the Islamic Revolution, the country's exchange rate system
remained a fixed exchange rate system, but with the emergence of the central bank's
capital flight atmosphere to contain and control this stream implemented controls .
The beginning of imposed war caused foreign exchange earnings faced many constraints,
reduced export opportunities, increased demand for imports, and lower world oil prices.
At the same time, adopting a policy of import substitution, which began a decade before
the victory of the revolution, increased the need for high-capacity industries whose major
equipment was imported to foreign exchange earnings. On the other hand, the import of
essential commodities needed by society as well as the increasing costs of development
projects also required access to foreign exchange resources. Given the limited foreign
exchange earnings of the country, channeling and optimally allocating these resources
was crucial. After the imposed war and the initial reconstruction of the exchange rate,
unification became one of the priorities of the country's economic transformation. The
exchange rate unification policy was first introduced in 1993. The exchange rate policy
in Iran in 1993 was followed by a sharp increase in the exchange rate due to the
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imbalances in payments, mainly due to falling oil prices and the problem of overdue
payments. In general, the policies implemented were not successful because of the lack
of coordination and requirements in all of the country's policies for exchange rate
unification.
Since the policy of exchange rate unification with the approach of achieving a more
flexible exchange rate system plays an important role in improving the performance of
different economic sectors, this policy was re-applied in 2002 and the types of existing
rates were abolished. In 2002, the implementation of a unification exchange rate policy
using the financial support from the Central Bank's foreign exchange reserves
significantly reduced the free and official exchange rate gap considerably and provided
relative stability in the foreign exchange market. The managed floating exchange rate
system was in operation from 2002 to mid-2010. During these years, in addition to the
exchange rate derived from oil and gas exports, the continued increase in non-oil exports
served as a resource for managing the exchange rate market. Although there were views
that the exchange rate adjustment was commensurate with the difference in domestic
and foreign inflation rates, the existence of sufficient foreign exchange resources made
it difficult to maintain relative stability in the foreign exchange market.
Since 2010, with the imposition of new sanctions against the country's banking system,
due to a tightening of the program and the restriction of oil revenues, the exchange rate
hike accelerated. The unofficial rate of the dollar at the end of 89 was about 10400 Rials,
which at the end of March the following year went up to the 19000 Rial. In fact, the
unofficial dollar rate in the 2011 saw an 80 percent growth. Following these
inflammations, the central bank of Iran started raising the official rate and announced
the official dollar rate at 12260 Rial. The exchange rate shock of 2011 continued in the
following year, as the exchange rate fluctuations were very high and the dollar in the
open market experienced a price of 40000 Rial per 1 US$. In the second half of the year
2017 a new round of volatility in the exchange rate market began that was exactly the
same as in the 2011 and 2012. The increase in the price of the dollar accelerated since
December 2017 and gradually increased to the limit of 48, 500 Rial per USD.
2. Literature review
Since the 1990s, researchers have focused more on empirical studies of exchange rates.
Since then, most empirical research has studied the effect of ERPT on prices in particular
industries, specific countries, or groups of countries depending on the general
characteristics of their macroeconomics. For example, Feinberg (1989) and Knetter
(1993) have empirically examined price adjustment in terms of degree of market
concentration, relative shares of domestic and imported products, import penetration,
and exchange rate fluctuations. Studies such as Devereux and Yetman (2010) concluded
that the ERPT is significantly and positively correlated with the average inflation rate and
the low inflationary environment leads to the pass-through of low exchange rates to
import prices. Also, Campa and Goldberg (2005), Taylor (2000), and Frankel (2012)
consider exchange rate volatility as an important factor in ERPT.
In addition, a large number of studies have examined the degree of exchange rate
domestic and import prices for developing and emerging countries as an inter-country
panel. Researchers such as McFarlane (2009) and Razafimahefa (2012) examined the
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degree of ERPT on consumer prices and import prices for developing countries and
emerging markets. These articles generally found that the degree of ERPT for developing
and emerging countries was significantly greater than that of advanced countries. In
addition, ERPT can have an asymmetric effect on prices, depending on the decrease or
increase in the value of the exchange rate and its absolute fluctuation. In Kohlscheen's
(2010) paper, using the VAR model, the degree of ERPT to consumer prices has been
examined for a number of countries during their floating exchange rate regimes. The
results showed that for countries with higher nominal exchange rate fluctuations and less
commercial diversification, higher exchange rates pass. In another study, Ito & Sato
(2008), by examining ERPT in East Asian countries, concluded that the degree of ERPT
along the distribution chain decreased and the highest rate of ERPT happen respectively
in import, producer and consumer prices. In this regard, Ghosh (2013) has examined the
ERPT for a number of Latin American countries over the past four decades. The results
showed that the degree of ERPT decreased over time.
Many studies have also been conducted on the pass-through of exchange rates in
different countries over time. Justel & Sansone (2015), by examining the degree of ERPT
using the VAR model for Chile, concluded that the rate of pass-through of exchange in
Chile has been decreasing over time. Espada (2013) investigated the degree of ERPT in
Mexico using the VAR model. The results indicate that the ERPT was not statistically
significant. In the same period, Peón & Brindis (2014) found that the degree of ERPT
decreased along the chain. In another paper, Rincón-Castro & Rodríguez-Niño (2016),
using the bayesian approach by endogenously expressing the ERPT and the economic
situation, concluded that the ERPT is greater if, (1) consumer inflation is accelerated, and
its fluctuation should be large (2) real exchange rate over-valued (3) positive output gap
(4) low openness of trade (5) high commodity prices (6) interbank interest rate Be low.
Masha & Park (2012) examined the degree of ERPT to consumer and producer prices in
the Maldives using recurrent VAR analysis. The results show a high but incomplete degree
of ERPT compared to other countries. Arslaner (2014) used an error correction model to
estimate the ERPT in Turkey for the period 1986–2013. The results indicate a significant
degree of ERPT to consumer inflation. Savoie-Chabot & Khan (2015) also examined the
degree of ERPT to consumer prices. They found that ERPT played an important role in
recent inflation dynamics in Canada. Tunc, C., & Kilinc (2018) also examined the ERPT in
Turkey using a structured VAR approach. Their results indicate that achieving the price
stability target permanently in Turkey becomes a major challenge in a volatile global
financial market, due to a high ERPT.
Much of the literature on ERPT has shown that exchange rate fluctuations are only
partially transmitted to domestic prices, whose effect is also lost through the production
chain. Exchange rates pass through domestic prices through several channels. From
direct effects through energy and other commodity prices to indirect effects through
import prices, wage formation and profit margins (Bacchetta & Van Wincoop, 2003;
Burstein & Gopinath, 2014; Ito & Sato, 2008; McCarthy, 2007). Even in the case of
internationally tradable goods, different forms of market segmentation and nominal
adhesions may explain the incomplete ERPT. In relation to the lower sensitivity of
domestic prices to exchange rate fluctuations, a number of structural factors include the
degree of competition between exporting and importing firms (Amiti et al., 2016), the
frequency of price adjustments (Devereux & Yetman, 2003; Corsetti et al., 2008;
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Gopinath et al., 2010), Trade Composition (Goldberg & Campa, 2010), Global Value Chain
Involvement (Georgiadis et al., 2017), Foreign Exchange Trade Share (Casas et al.,
2016; Gopinath, 2015) and the use of exchange rate risk coverage tools (Amiti et al.,
2014). Also, a credible monetary policy framework that supports anchor inflation
expectations can serve as an effective approach to reducing ERPT to consumer prices
(Carriere-Swallow et al., 2016; Gagnon & Ihrig, 2004).
In addition to the country-specific structural factors and characteristics, the nature of the
macroeconomic momentum that causes the exchange rate fluctuation plays a key role in
determining the size and intensity of the ERPT (Comunale & Kunovac, 2017; Forbes et
al., 2018; Shambaugh, 2008). This reflects the fact that impulses that affect the
exchange rate simultaneously affect activity, profit margins, productivity, and other
factors that contribute to shaping price and inflation expectations. Helmy et al. (2018),
using monthly Egyptian data for the period 2003 to 2015, concluded that the pass-
through of the exchange rate to the three price indices (import, producer and consumer)
in Egypt was relatively significant and incomplete. Of course, the degree of ERPT to
consumer prices was higher than producer and import prices. Ha et al (2019) estimated
SVAR models for a set of 47 countries and concluded that different domestic and global
shocks were an important factor in explaining the degree of exchange rate across
countries. In addition, the specific features and conditions of each country include the
policy frameworks for implementing the next influential monetary policy. Furthermore,
the exchange rate was lower in countries with flexible exchange rates and credible
inflation targets. Finally, empirical evidence has shown that the central bank's degree of
independence influences the degree of ERPT to prices. Adekunle & Tiamiyu (2018)
examined the asymmetry of ERPT to consumer prices in Nigeria during the period 2001–
2015. The results showed that in the short run, consumer prices had comparable
expectations and incomplete exchange rate pass.
Although there have been many foreign studies on the degree of ERPT, there are still few
domestic studies. In continuation, the internal empirical studies are briefly reviewed.
Mesbahi et al. (2017) assessed the degree of ERPT of import prices by emphasizing the
role of volatility in oil revenues. Tayebi et al. (2015) conclude that exchange rate inflation
is incomplete with different price indices, but exchange rate fluctuations cause
fluctuations in import, consumer and producer price indices and part of the variability of
domestic inflation over the period. The case is explained. Kazerooni et al. (2012) have
proposed that by simultaneously implementing the monetary inflation targeting system
and the floating exchange rate system, the exchange rate pass rate will be reduced.
Khoshbakht & Akhbari (2007) have shown in their study that the ERPT changes on the
import price index is more than the consumer price index. In their study, Shajari et al.
(2005) concluded that the degree of ERPT in Iran is incomplete and that real exchange
rate changes have a positive and significant effect on the price of imported goods.
3. Research Method
The purpose of this study is to investigate the dynamic relationship between factors
affecting the exchange rate in Iran. Hence, the VAR model proposed by Sims (1980)
assumes that all variables are endogenous in a macroeconomic model without any
constraints on their relationships. The VAR return form contains not only the endogenous
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variable interrupts, but also the uninterrupted values of other endogenous variables.
Petroleum exports to oil-rich countries are important sources of foreign exchange
earnings, but the outsourcing of these incomes leads to widespread uncertainty and
instability in their economies, their economic policies, and their economic policies. Given
the high reliance of the Iranian economy on oil revenues and the randomness of oil price
shocks, the macroeconomic environment has been affected and the combination of these
factors has led to uncertainty. The volatility of the economy and the bad macroeconomic
environment lead to an increase in the exchange rate. As oil revenues rise, the
uncertainty of the exchange rate declines, and the exchange rate slows.
The rise in oil prices and the resulting revenues can provide a boost to the exchange rate
and import prices by increasing import demand. Therefore, the rise in oil prices is in the
first phase of the impulse distribution chain, affecting other model variables. The variable
rate of economic growth rate of production in the local literature on the exchange rate
can be cited as an indicator of domestic demand pressure. Economic growth is driving
demand and, given production's inability to meet demand, this will increase domestic
demand and ultimately lead to increased demand for imported goods, rising exchange
rates and rising prices for imported goods. The consumer price index in the impulse
distribution chain is after the exchange rate because the effect of the exchange rate on
the import price through imported foreign inputs affects the consumer price. Finally, the
central bank response function is estimated in which the demand function relates money
growth to other variables in the model, since monetary policy may reflect exchange rate
fluctuations (McCarthy, 2007).
Following McCarthy (2007) in the present study, the money supply growth equation is
considered as a central bank response function. In oil-exporting countries, rising oil prices
and subsequently rising oil revenues lead to a massive injection of money into the
economy, so the money supply is also a function of oil prices and revenues. According to
what is said above, the model is based on the study (McCarthy, 2007) and has the
following order for the variables.
Where oil price inflation, annual GDP growth, changes in nominal exchange,
CPI
t
consumer price inflation,
PPI
t
producer price inflation,
IPI
t
import price inflation,
and is money growth rate. Within this framework, it observes the dynamic effect
of exchange rate momentum on price indices along the distribution chain. According to
(McCarthy, 2007), consumer price inflation is composed of seven components at each
stage. The first component is expected inflation based on information available in the t-
1 period. The effects of supply and demand shocks on inflation at this stage are used as
the second and third components in the t-period. The fourth component is the effect of
exchange rate momentum on inflation. The next component includes the effects of import
price shocks, producer prices and consumer prices on inflation in earlier stages of the
chain, and the last component includes the impact of all steps in the distribution chain.
Structural impulses are obtained from VAR residues using the Cholesky variance-
covariance matrix analysis. Thus, oil price inflation ( ) is used as a supply-side and
oil
t
t
y
t
e
1
t
M
oil
t
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output growth ( ) as a demand-side, in addition, the model involves money as a
monetary policy variable that responds to other variables through the response function.
Under this assumption, the impulses in this VAR system can be represented by a
recursive VAR system as follows:
Where the supply shocks, demand shocks, exchange rate shocks,
consumer’s price inflation shocks,
PPI
t
producer price inflation,
IPI
t
import price inflation
and money supply shocks. Expresses the expectations of the variables in
terms of information available at the end of period t-1, which represents the time period
t. Conditional expectation equations can be replaced by linear predictions in terms of 5
endogenous variable interruptions.
In the following, the functions of instant reaction of consumer price inflation to
uncorrelated exchange rate shocks will be presented. In addition, impulse identification
using Cholesky's analysis generates the subject, identifying impulse supply and
aggregate demand. Here the assumptions are assumed to be serially uncorrelated and
not correlated over a period.
4. Results and discussion
4.1. Stationary results
In order to accurately model the VAR model, static and cointegration tests have been
performed for the characteristics of the studied data and the results are reported in Table
(1) and Table (5). In the first step, the static data are examined using the generalized
Dickey Fuller root unit test (ADF). Table (1) shows the results of the single root test for
model endogenous variables.
Table 1: unit root test results
variable
ADF statistic
probability value
degree of accumulation
unit root test results at the level of variables
OIL
-1/69762
0/4195
Non-Stationary
ER
-1/31789
0/6586
Non-Stationary
CPI
1/36534
0/9856
Non-Stationary
M1
-0/15997
0/9321
Non-Stationary
GDP
1/90123
0/9872
Non-Stationary
PPI
0/51239
0/9543
Non-Stationary
IPI
-1/29654
0/62367
Non-Stationary
t
y
1
t
M
oil
t
y
t
e
t
cpi
t
1M
t
1t
E
−
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Unit root test results in first difference of variables
D(OIL)
-11/76813
0/0000
Stationary
D(ER)
-10/45789
0/0000
Stationary
D(CPI)
-11/34587
0/0000
Stationary
D(M1)
-7/14821
0/0000
Stationary
D(GDP)
-6/32167
0/0004
Stationary
D(PPI)
-7/67294
0/0000
Stationary
D(IPI)
-8/52312
0/0000
Stationary
4.2. Optimal lag selection
In order to select the appropriate lag length for estimating the VAR model, several tests
such as sequential modified LR test, lag exclusion Wald test (omitting statistically
meaningless lags), Hannan Qwuinn information criterion (HQ), Akaike information
criterion (AIC), Schwarz information criterion (SC) and the final prediction error (FPE)
were assessed. By choosing the appropriate lag in the VAR model, it will prevent the over
fit by limiting the length of the small sample intervals. It also minimizes the incorrect
stipulation of the model by not selecting too small interrupts. The benchmark length
interval tests are shown in table 2. Sequential modified LR test, final prediction error
(FPE) and Akaike information criterion (AIC) suggest using the VAR model (3). Therefore,
the VAR model is estimated with three intervals in this study.
Table 2: Results of optimal lag length
HQ
SC
AIC
FPE
LR
Log L
Lag length
6.78
6.61*
6.4
1.09 E-04
NA
-437.54
0
6.11*
6.78
5.9
6.3 E-05
149.42
-368.32
1
6.51
7.35
5.8
6.6 E-05
53.52
-332.21
2
6.63
8.18
5.8*
6.1E-05*
35.13*
-273.43
3
7.52
8.39
5.62
6.8 E-05
46.17
-232.7
4
The results of the parent variable omitted test are presented in table 3 to determine
whether the intervals containing significant information were omitted from the model.
The results indicate that the three interruptions in the VAR system are mutually
significant.
Table 3: lag exclusion Wald test results
Joint
DM1
DMPI
DCPI
DEX
DGDP
DOIL
159.41
(0.01)
31.1
(1.13)
4.26
(0.64)
40.11
(0.01)
19.15
(0.00)
11.03
(0.13)
15.9
(0.01)
Lag 1
72.18
(0.00)
9.32
(0.19)
12.18
(0.08)
7.912
(0.35)
14.99
(0.01)
14.2
(0.00)
3.47
)0.72)
Lag 2
89.1
(1.13)
14.7
(0.01)
14.6
(0.01)
11.7
(0.04)
5.891
(0.45)
24.01
(0.08)
17.3
)0.05)
Lag 3
* The numbers in parentheses represent the P-value
In addition, the Lagrange coefficient of serial residual correlation (LM) in the VAR model
was calculated with the null hypothesis of no serial correlation.
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Exchange rate pass-through to price indices in Iran
Mohsen Mohammadi Khyareh
152
Table 4: LM Serial Correlation Test Results
Hypothesis Zero: No Serial Correlation at Order H Interval
probability value
LM statistic
interrupts
0/2212
42/21341
1
0/5849
35/88122
2
0/1103
46/32723
3
0/3122
39/31674
4
0/0547
44/5523
5
4.3. Co-integration test
The results of the co-integration test between variables using the Johansen coefficient
test are presented in table 5.
Table 5: Cointegration test results
Probability
Value
Critical
5%
Trace
Statistical
Probability
Value
Critical
5%
Eigen
value
Number of
Equations
0.0000
91.6
179.02
0.0000
31.3
61.5
Non*
0.0000
68.7
120.28
0.0001
29.7
51.2
one vector
0.0000
46.6
76.292
0.0001
20.6
34.8
two vectors
0.001
39.2
51.998
0.012
19.1
25.9
three vectors
0.009
25.653
46.3219
0.022
16.56
19.631
four vectors
0.013
23.674
26.5916
0.0312
14.82
16.442
five vectors
0.023
13.674
15.5916
0.0467
9.82
10.442
six vectors
* Rejects hypothesis at 5% level
Due to the existence of seven model variables and the results of the special effects and
maximum likelihood tests, maximum six coefficients are accepted. As a result, the
attempt is made to estimate the VECM model by considering six coherent relationships.
4.4. Estimation of ERPT coefficients
Using the instantaneous reaction function, the cumulative pass-through coefficients are
calculated by dividing the cumulative instantaneous reaction of prices after m period by
the cumulative instantaneous reaction of the exchange rate to the exchange rate
momentum after m period. ERPT at time t is defined as follows:
Where, P and E are respectively the change in the cumulative price and the change in
the cumulative exchange rate after m period. Table 6 shows the ERPT to the consumer,
producer and import price indices calculated over a 20-year time horizon. ERPT to
consumer, producer, and import prices ranged from 14.68%, 15.45 % and 18.22 % in the
,
,
Pr
t t m
t
t t m
ice index
ERPT
Exchange rate
+
+
=
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Exchange rate pass-through to price indices in Iran
Mohsen Mohammadi Khyareh
153
first period to 51.78%, 53.15 % and 88.14 % in the next 13 periods, respectively. The
momentum of the exchange rate rises. It can also be seen from figure 2, that the ERPT
to import and producer prices is higher than consumer prices. This result is consistent
with the empirical findings of research conducted in Iran and the theoretical foundations
of ERPT. Because, exchange rate shocks will have the greatest impact on the prices of
finished goods and imported inputs, and then the inflation of imported inputs will affect
the producer price and then the consumer price in the last place. . According to the
results, it can be said that the exchange rate pass through to price indices in Iran is
incomplete, which is consistent with the experimental results of exchange rate analysis
in Iran such as (Bahrami et al., 2014; Tayebi et al., 2015; Heydari & Ahmadzadeh, 2015;
and Ebrahimi & MadaniZadeh, 2016) are compatible. Finally, about 46.74%, 50.38% and
88.53 % of the exchange rate rise eventually are reflected in consumer, producer and
import prices, respectively, after 20 periods of shocks. In addition, the results show that
the degree of ERPT decreases along the distribution chain and the highest rate of ERPT
occur at import prices, producer prices and consumer prices, respectively. The results of
the study are Ito & Sato (2008) and Peón & Brindis (2014). The results of the exchange
rate transition in the results also show that the degree of ERPT has decreased over time,
which is in line with the Ghosh (2013).
Table 6: ERPT Coefficients
Consumer Price
Producer Price
Import Price
Period
14.68
15.45
18.22
After 1 period
16.73
18.34
20.45
after 2 period
24.68
25.45
48.13
after 4 period
27.44
28.37
51.22
after 5 period
45.61
47.45
79.21
After 8 period
50.43
52.35
82.37
after 10 periods
51.78
53.15
88.14
After 13 rounds
45.62
47.59
83.67
after 16 periods
46.74
50.38
88.53
after 20 periods
Conclusion
When implementing anti-inflation economic policies in high-inflation countries such as
Iran, it is necessary to analyze the impact of ERPT on the price index. On the other hand,
exchange rate changes are very important and have a huge impact on the
macroeconomic indicators of countries. Therefore, for an economy that is committed to
maintaining price stability, it is very important to adjust exchange rate changes. In this
way, countries can assess how the impact of exchange rate shocks affect their economies
and can take preventive measures and policies based on this information. Using the
cumulative transient response function derived from the recursive VAR model, the ERPT
to consumer, producer, and import prices changed from 14.68%, 15.45%, and 18.22%
in the first period to 51.78%, 53.15%, and 88.14 % in 13 periods after exchange rate
shock. The instantaneous response function of the price index to the exchange rate shows
that the exchange rate impulse has a positive and significant impact on the inflation of
the price index.
The analysis of variance also confirmed the impact of the ERPT, because in view of the
high share of imported goods in the consumer basket and the concentration of imports
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Exchange rate pass-through to price indices in Iran
Mohsen Mohammadi Khyareh
154
in major manufacturing industries, the exchange rate rises led to higher prices of
imported goods. And because of the increase in oil revenue, the demand for the entire
economy is also increasing, so prices are also rising. The instantaneous response function
of the price index to exchange rate changes shows that exchange rate shocks have a
positive impact on inflation. The results of the analysis of variance confirmed the role of
ERPT in explaining the form of price index fluctuations. Therefore, in view of the research
results and the importance of exchange rate fluctuations in explaining inflation in the
Iranian economy, the central bank’s monetary policy should aim to reduce the passing
level, and policies to limit exchange rate fluctuations will contribute to the goal of price
stability. Similarly, in small open economies, central bank loans are particularly important
for exchange rate fluctuations due to the favorable impact of exchange rates on
macroeconomic variables (such as inflation). An inflation targeting system must also be
established in the country’s economy, because the impact of low exchange rates on
domestic prices gives people greater freedom to implement independent monetary
policies, especially through inflation targeting. The results also show that the transfer
rate of exchange rate changes to the price index is not as complete as other studies. The
transfer of the exchange rate is not complete, because the price of imported goods is not
only affected by the exchange rate, but also by other factors (such as increased domestic
demand).
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