Download Is low inflation really causing the decline in exchange rate pass

Is low inflation really causing the decline in exchange
rate pass-through?
Reginaldo P. Nogueira Júnior
Fundação João Pinheiro, Brazil
Miguel A. León-Ledesma
University of Kent, UK
(Preliminary version)
This draft: August, 2008.
Abstract: Recent literature has argued that exchange rate pass-through (ERPT) has been
declining in many countries after the adoption of inflation targeting regimes, following
the change of the inflation environment. We use a state-space model to estimate the
time-path of ERPT for some developed and emerging market economies, allowing for
the ERPT coefficient to depend on lagged inflation. We then apply likelihood ratio tests
in order to determine causality between inflation and ERPT. The results reinforce the
view that the impact of exchange rate on prices has been declining, but offer only weak
evidence that lower inflation is causing it.
Keywords: Exchange Rate Pass-Through, State-space Models, Causality Tests.
JEL Classification: E31, E42, E52, E58, F31, F41.
Acknowledgments: We would like to thank John Driffill, Mathan Satchi, Eduardo
Sanchez and Andrew Harvey for their insightful comments. Usual disclaimer applies.
Address for correspondence: Reginaldo P. Nogueira Júnior. Fundação João Pinheiro.
Alameda das Acácias, 70. Belo Horizonte, Minas Gerais. Brazil. 31.275-150. E-mail:
[email protected].
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Is low inflation really causing the decline in exchange
rate pass-through?
1. Introduction
The pass-through from exchange rate changes into domestic inflation seems to have
been declining in many countries in recent years, especially after the adoption of
Inflation Targeting (IT) regimes (see, for example, Campa and Goldberg, 2005; Bailliu
and Fujii, 2004; Gagnon and Ihrig, 2004; Bouakez and Rebei, 2007; Choudhri and
Hakura, 2006). The basic explanation found in the literature for this decline is that it is a
by-product of the low inflation environment of the 1990s. In this paper we take part of
this debate, and present further econometric evidence on the decline of exchange rate
pass-through (ERPT) for some developed and emerging market countries that adopted
IT. We apply a methodology that allows us to test directly whether the low inflation
environment brought about by the adoption of IT is causing the decrease in ERPT.
Taylor (2000) was the first to provide an interpretation of the declining ERPT
related to the lower inflation environment of the last decade. He argued that with
staggered prices firms are more likely to pass-through cost changes, including those
from the exchange rate, when inflation is high. In this sense, ERPT would be
endogenous to a country’s inflation performance. Campa and Goldberg (2005), Gagnon
and Ihrig (2004), Choudhri and Hakura (2006), among others, have analyzed this
relationship, finding a positive correlation between ERPT and inflation indicators.
Mishkin and Savastano (2001), Leiderman and Bar-Or (2000), and SchmidtHebbel and Tapias (2002) argue that ERPT depends on the monetary policy credibility,
and therefore it is likely to decline overtime as the central bank’s commitment as an
inflation-fighter becomes clearer. This hypothesis is particularly important for countries
that adopted IT, as the reaction of inflation to a nominal shock should depend on how
much inflation expectations are in line with the inflation target. Hence, the impact of a
nominal shock on inflation, such as a currency depreciation, should be lower the higher
the monetary policy credibility.
2
Choudhri and Hakura (2006) relate the credibility hypothesis for the declining
ERPT with Taylor’s (2000) hypothesis, arguing that regimes that make a stronger effort
in stabilizing short-run inflation so it keeps in track with its long-run target, are able to
maintain low inflation rates. This view once again links the degree of ERPT in an
economy to its long-run inflation performance.
Most of the evidence of the declining ERPT in the literature is provided by
splitting the sample estimations, as in Campa and Goldberg (2005), Gagnon and Ihrig
(2004) and Choudhri and Hakura (2006), or by rolling regressions, as in Reyes (2003).
Those techniques do not provide precise timing of the exchange rate parameter shift. It
is also often the case with rolling regressions that the timing of the parameter shifts
crucially depends on the size of the windows. Three exceptions to these approaches are
Kim (1990) that applies the Kalman Filter to US data until the mid-1980s, Amstad and
Fischer (2005) whose approach is an application of event-study procedures used in
empirical finance to Swiss data from 1993:05 to 2005:05, and Sekine (2006) that
estimates ERPT for 6 industrial economies using a time-varying parameter with
stochastic volatility model. The latter finds that ERPT into consumer inflation has
declined over time for all economies analyzed, and that there is high correlation
between the estimated ERPT and the observed inflation rate.
Empirical investigations on the causes of this decline encounter the difficulty
that ERPT is an unobservable variable. In this sense, checking whether inflation is
driving down ERPT is not an easy task. Moreover, the previous literature has tested
Taylor’s (2000) hypothesis that the inflation environment is inducing a decrease in
ERPT by analyzing cross-country correlations between inflation and ERPT. The
problem with this procedure is that, as widely acknowledged, correlations do not imply
causality, and a positive correlation between the estimated ERPT and the rate of
inflation may imply not that lower inflation induces a lower ERPT, but that a lower
ERPT provides a better environment for low inflation.
Following these issues, in this paper we model ERPT by means of a state-space
model, allowing for time variation in the coefficients. State-space models have been
used in the literature to model unobserved variables, such as rational expectations,
measurements errors, missing observations, and permanent income. In this paper we use
a state-space specification in order to model the unobservable variable ERPT. The state-
3
space model presents a flexible structure that allows testing the casual relationships
postulated in the literature, in terms of Granger causality tests. In this sense the main
advantages of using a state-space model are that it allows for modelling unobserved
variables and testing for temporal causality.
We develop a backward-looking Phillips curve augmented with exchange rate
and import prices. In our specification we allow ERPT to depend on lagged inflation.
We then apply likelihood ratio tests in order to determine the direction of causality
between inflation and ERPT. To our knowledge there is no other study that applies such
tests for these variables. An additional advantage of our methodology is that we observe
the evolution of ERPT without imposing any break in the series, letting the data itself
tell us what is happening with ERPT and its relationship with inflation.
Our results show that ERPT has been gradually declining for all the economies
under consideration, as suggested by the previous literature. Regarding the direction of
causality between inflation and ERPT, the results provide only weak evidence in favor
of Taylor’s (2000) hypothesis, as for only a minority of the countries analyzed there
seems to be bi-directional causality between inflation and ERPT.
The remainder of the paper is structured as follows. Section 2 briefly discusses
the recent literature on the declining ERPT issue. Section 3 presents the methodology
applied. Section 4 shows the results. Section 5 concludes.
2. Literature review
There is plenty econometric evidence that the exchange rate pass-through (ERPT) has
been declining in many countries in recent years. As this decrease occurred in a period
of declining inflation, many studies started to consider the possibility that the ERPT is
endogenous to the inflation regime. In favor to this argument lies the fact that most of
the economies for which there is evidence of a falling ERPT have adopted an Inflation
Targeting (IT) regime at some point during the 1990s, and have successfully used this
monetary framework to drive down inflation to low and stationary levels.
In a popular paper, Taylor (2000) was the first to provide a theoretical model
relating the decline of ERPT observed in empirical investigations to the low inflation
environment of the 1990s. Taylor (2000) explained this relationship in terms of a model
4
of firm behavior based on staggered price setting and monopolistic competition. As
firms set prices for several periods in advance their prices respond more to cost
increases due to exchange rate movements if cost changes are perceived to be more
persistent. As regimes with higher inflation tend to have more persistent costs, he
argued that a high inflation environment would thus tend to increase ERPT. In other
words, ERPT would be endogenous to a country’s inflation performance.
Campa and Goldberg (2005) argue that an important implication of Taylor’s
(2000) argument is that there is a virtuous circle wherein low inflation leads to reduced
ERPT, less inflationary implications of monetary expansions, and continued low ERPT.
This circle, however, is rather fragile, as it can be overcome by adverse supply shocks.
Campa and Goldberg (2005) tested Taylor’s (2000) hypothesis in terms of correlation
analysis between the estimated ERPT and a set of macro and micro variables, and
concluded that although the argument has some statistical merit, it is not of first
importance for the low and medium inflation countries of the OECD. For those
countries they argued that the lower ERPT was brought about by a change in the
composition of the import bundle1.
Choudhri and Hakura (2006) also found strong evidence of a positive correlation
between ERPT and the average inflation for 71 countries, including developed and
emerging market economies, as well as some countries that adopted IT. They explored
the influence of other macroeconomic variables as well, but found that average inflation
dominates in explaining differences in observed ERPT. Similar results were found by
Ca’Zorzi, Hahn and Sanchez (2007) for a sample of emerging market economies. They
have observed that emerging markets with moderate rates of inflation tend to have a
degree of ERPT compatible to developed economies.
Bailliu and Fujii (2004) have presented evidence from a panel-data set of 11
industrialized countries that adopted IT. Their findings support the hypothesis that
ERPT declines with a shift to a low-inflation environment brought about by a change in
the monetary policy regime. More specifically, the results suggest that ERPT declined
following the inflation stabilization that occurred after the adoption of IT in the early
1
Choudhri and Hakura (2006) argued that Campa and Goldberg (2005) have analysed ERPT to import
prices and not to consumer prices. Hence, they analysed the price behaviour of foreign firms, which may
not be strongly related to the home inflationary environment. In this sense, evidence on the ERPT to
domestic prices would provide a more appropriate test of Taylor’s (2000) view.
5
1990s, but did not decline following a similar episode in the 1980s. Bailliu and Fujii
(2004) argued that a potential explanation for this finding is that changes in the
monetary policy regimes implemented in the 1990s were perceived as more credible
than those carried out in the 1980s.
Mishkin and Savastano (2001), Leiderman and Bar-Or (2000), Cespedes and
Soto (2005) and Schmidt-Hebbel and Tapias (2002) have also argued that ERPT
depends on the credibility of the monetary policy. The basic hypothesis is that ERPT is
likely to decline over time as the country’s anti-inflationary commitment becomes
clearer2. As discussed by Reyes (2003) this finding is particularly important for
countries that adopted IT. In a credible regime, expectations are more likely to be in line
with the authority’s inflation target and therefore will be less influenced by short term
exchange rate movements.
Levin, Natalucci and Piger (2004), Schimidt-Hebbel and Tapias (2002), Minella
et al. (2003) and others have tested the inflation response to nominal shocks, such as
those from the exchange rate, in IT regimes. These studies are generally based upon the
analyses of the impulse responses of VAR models. The results show that shocks are
weaker and less persistent under IT frameworks, suggesting a reduced role for priceindexation and reinforcing the importance of the inflation targets to anchoring inflation
expectations and, as consequence of that, to lowering ERPT3.
Choudhri and Hakura (2006) provide a link between the low inflation and the
high credibility hypothesis for the falling ERPT. They derive a negative association
between ERPT and the degree to which monetary policy offsets short-run price
deviations from the long-run target. This association basically arises because the ERPT
reflects the expected effect of monetary shocks on current and future shocks. A regime
that reacts aggressively to price deviations lowers ERPT by weakening the expected
future effect of shocks. As regimes that make a stronger effort in stabilizing short-run
inflation are also able to maintain low inflation in the long-run, they argue that in
empirical analysis the rate of inflation can be used as an indicator of the aggressiveness
of monetary policy response to short-run price fluctuations.
2
Eichengreen (2002) argued that emerging market economies tend to have a high ERPT as their
institutions lack credibility. His argument is that under imperfect credibility the market takes transitory
exchange rate shocks as permanent, thus influencing the degree of ERPT.
3
Levin, Natalucci and Piger (2004) and Minella et al. (2003) actually suggest using the degree of ERPT
as a proxy of monetary policy credibility.
6
A similar argument is used by Gagnon and Ihrig (2004) that explored the
relationship between inflation, monetary policy credibility and ERPT for 20 industrial
countries, half of them following an explicit IT regime. They have looked at the link
between ERPT and parameters estimated from Taylor-type monetary policy rules, but
failed to find a robust relationship. The hypothesis they wanted to test was that when a
central bank acts aggressively to stabilize inflation it tightens policy to offset any
inflationary effect from a rise in import prices. When the market realizes the central
bank’s intentions, they are less likely to pass-through cost increases, including those
coming from the exchange rate. Nevertheless, they were able to find similar results to
those of Choudhri and Hakura (2006), showing a strong correlation between the
estimated ERPT and the inflation environment, taken as the mean and the standard
deviation of the rate of inflation.
3. Methodology
A wide variety of time-series models can be written and estimated as special cases of a
state-space specification. Extensive examples of applications of state-space models can
be found in Harvey (1989). We use a state-space specification to model a time-varying
ERPT, as well as the variables that drive its dynamics.
There are three main benefits of representing a dynamic system in state-space
form. First, the state-space model allows unobserved variables (the state variables) to be
estimated with the observable model. Second, state-space models can be analysed using
the powerful Kalman Filter recursive algorithm, which is commonly used to estimate
time-varying coefficient models4. And third, a very important feature of state equations
is it flexibility, as they may contain exogenous variables and unknown coefficients, and
may also be nonlinear in these elements.
A state-space model usually consists of two sets of equations, the measurement
equations and the state equations. The Kalman filtering approach provides optimal
estimates for state variables based on the information from the two sources, the
measurement and the state equations.
4
The Kalman Filter can be described as a tool that enables the estimation of the state variables and the
parameters in a time-varying parameter model using maximum likelihood.
7
We estimate ERPT by means of a state-space model composed of the following
system of equations:
n
∆ Pt = α 1t ∑ ∆ Pt − i + α 2 t ∆ P * t −1 + α 3 t ∆ y t + α 4 t ∆ e t −1 + ε 1t
(1)
α1t = α1t −1 + ε 2t
(2)
α 2t = α 2t −1 + ε 3t
(3)
i =1
α 3 t = α 3 t −1 + ε 4 t
(4)
n
α
4t
= α
4 t −1
+ β 1 ∑ ∆ Pt − i + ε 5 t
(5)
i =1
Where ∆P is the inflation rate, ∆P* is the change in the price of imports, ∆e is
the exchange rate change, and ∆y is the output growth5. The terms ε t , 1…5, are
independent normally distributed errors, with zero mean and constant variance.
Equation (1) is the measurement equation, and equations (2) to (5) are the state
equations. When estimating the model we included more than one lag of the inflation
rate for some countries due to autocorrelation6. The α ’s, 1…4, are estimated state
coefficients. The system is estimated using the Kalman Filter technique. Note that we
imposed a unit-root in the state equations. As discussed by Sekine (2006) and Tombini
and Alves (2006) this is a very standard procedure in the literature on state-space
modelling for allowing possible level breaks or trend patterns to be captured7.
Equation (1) captures the traditional arguments of the literature on ERPT, and is
similar to that estimated in several papers, as Campa and Goldberg (2005), Choudhri
and Hakura (2006) and others. The equation represents a backward-looking Phillips
5
The problem of smoothing output to obtain output gap, in special through the use of the traditional HP
Filter is discussed in several papers, as King and Rebelo (1993), Harvey and Jaeger (1993), and Cogley
and Nason (1995). It is, for example, widely recognised that the smoothing parameter in the HP Filter
may be different for different countries. Besides, ad-hoc de-trending processes may eliminate valuable
information from the data. Nevertheless, we have also estimated the model using HP-filtered and Bandpass filtered output gaps, and the results were similar. Following this, we have opted to report only the
results using output growth.
6
The additional lags were included in equations (1) and (5).
7
When estimating the model we have also tested the inclusion of a constant in the measurement equation,
but it proved to be statistically insignificant, and was excluded from the model. We also estimated the
model with the state variables following an AR(1) process, but the state series found were too noisy to be
of any economic sense.
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curve, controlling for the exchange rate and the price of imports. As demonstrated by
Campa and Goldberg (2005), empirical specifications that seek to isolate ERPT should
introduce controls for the foreign costs, as without such controls the measured
relationship is a statistical correlation without specific economic interpretation in terms
of ERPT8.
In the specification of the exchange rate coefficient we included a coefficient of
lagged inflation rate. In this sense ERPT coefficient is a function not only of its past
value, but also of the inflation environment. This specification allows us to analyse the
explanatory power of lagged inflation over the ERPT, and thus if it can be considered to
be a temporally causal variable for the decline in ERPT.
To obtain time-series for the state variables we applied the Kalman Smoothing
procedure9. The smoothing uses all the information in the sample to provide smoothed
estimates of the states and of the variances. This procedure differs from the Kalman
Filtering in the construction of the state series, as this technique uses only the
information available up to the beginning of the estimation period.
3.1 Granger causality tests
The previous literature has analysed correlations between the ERPT and the rate of
inflation. The basic procedure has been the estimation of the ERPT for a large number
of countries, and then using the estimated ERPT as dependent variable in a crosscountry regression, controlling for a set of macroeconomic variables (see, for example,
Baqueiro, Diaz de Leon and Torres, 2003; Choudhri and Hakura, 2006; Gagnon and
Ihrig, 2004). This approach provides correlations and cannot be informative about the
causality between the variables. In addition, cross-country regressions may problems of
heterogeneity among the different countries included in the model, making its results
not as robust as those obtained in a time-series context. Providing evidence of causality
is of utmost importance with respect to ERPT and inflation issue, as it may be that low
ERPT is causing low inflation, and not the contrary as suggested by the literature.
8
Campa and Goldberg (2005) show that the price of imports in local currency is basically a function of
the price of imports in foreign currency, the exchange rate, and the importer’s mark-up.
9
Suppose that we observe the sequence of data up to time period T. The process of using all this
information to form expectations at any time period up to T is known as smoothing.
9
Following the system of equations (1) to (5) presented before, we use Likelihood
ratio tests for checking the null hypothesis H0: α4 = 0 and H0: β1 = 0. The first null
hypothesis states that the exchange rate coefficient is equal to zero, thus its rejection
means that ERPT Granger causes inflation. The second null hypothesis checks if the
previous causality also runs in the other way, i.e. if inflation causes ERPT, hence
representing a formal test for Taylor’s (2000) proposition.
The steps for testing the causalities are as follows. First, we estimate the
unrestricted model and obtain the log-likelihood. Then, we estimate a restricted model,
with the coefficient under consideration equal to zero, and obtain the new loglikelihood. To the test the hypothesis that the coefficient is indeed equal to zero, we
apply the following test:
λ = 2 x (ULL – RLL)
(6)
Where ULL is the unrestricted log-likelihood and RLL is the restricted loglikelihood. When the sample size is large, it can be shown that the likelihood ratio test
statistic λ follows the chi-square distribution with degrees-of-freedom equal to the
number of restrictions imposed by the null hypothesis. If the statistic λ exceeds the
critical value at the chosen level of significance, the null hypothesis is rejected, in which
case the coefficient is not zero, and hence the variable belongs to the regression model.
This implies that the variable improves the model’s prediction.
Quarterly data was collected for 16 countries that adopted IT that may be
divided in two groups: the first one comprises developed economies (Australia, New
Zealand, Norway, Switzerland, Sweden, Iceland and United Kingdom), and the second
one is composed of emerging markets (Brazil, Czech Republic, Mexico, Israel, South
Korea, Thailand, Chile, Hungary and Poland). The period of estimation for each country
and the dates of adoption of IT can be seen in Table 1.
Data was obtained from the IMF International Financial Statistics database. The
inflation rate is the rate of growth of the consumer price index. Exchange rate data is the
change of the national currency per unit of dollar. A positive variation means
depreciation of the national currency, and a negative one means appreciation. Output
10
data is the quarterly real GDP growth10. Import prices are defined as the change in the
index of dollar price of imports for each country. This data was not available for Mexico
and the Czech Republic, so as a proxy of the foreign costs faced by those countries we
used the change in the index of international commodities. The data on real GDP and
inflation were seasonally adjusted. Unit root tests show that the variables included in the
model can be treated as stationary11.
4. Results
As discussed in the methodology we estimated a time-varying ERPT in terms of a statespace model, using the Kalman Filter to obtain the time-path of the state variables.
Table 2 shows some basic results of the estimated model. Figure 1 plots the estimated
ERPT into consumer prices, together with the rate of inflation. As discussed before the
time-series of the state coefficients were constructed by smoothing the states estimates.
Our time-varying parameter model suggests that ERPT has indeed declined over
time, but particularly after the adoption of the IT framework. The figures also suggest
that this decline took place somewhat more gradually than when compared to estimates
obtained from rolling regressions, such as those provided by Reyes (2003) and Sekine
(2006), although it holds resemblance to those12. An interesting point is that it is
possible to see the impact of exchange rate and confidence crises in influencing the
degree of ERPT as, for example, Brazil (2002), Mexico (1995) and Czech Republic
(1997). Furthermore, it is clear that there is a strong positive correlation between
inflation and ERPT, which is in line with findings of the previous literature. However,
as discussed before, instead of just observing correlations, as in the previous literature,
our approach to estimating ERPT allows us to analyse casual temporal patterns. The
results are presented in the Table 3.
10
In the case of Brazil the industrial production index was used. Due to data unavailability, Iceland’s
model was estimated without the output control variable.
11
We applied ADF, KPSS and Phillips-Perron unit root tests. The results are not reported here for reasons
of space, but are available upon request.
12
Sekine (2006) observes that rolling regressions tend to yield abrupt changes depending on whether or
not a specific sample is in the window. Because of this dependence on the size of windows, rolling
regressions do not provide precise timings of the changes in parameters. In addition to this, smoothed
series tend to produce more gradual changes than filtered ones. However, as argued by Sims (2001),
smoothed series provide more precise estimates of the actual time variation than filtered ones.
11
A salient result of the likelihood ratio tests is that for most countries in our
sample we were able to reject the null that the ERPT coefficient is equal to zero, and in
most cases at the 5% confidence level. This result makes it clear that inflation depends
on ERPT, or in other words, that ERPT Granger causes inflation. Taylor’s (2000)
hypothesis that a low inflation environment leads to a lower ERPT means that this
causality should ran also in the opposite direction. However, this result applies only to a
few countries. In other words, we were able to determine causality from ERPT to
inflation for all the economies, but bi-directional causality between the two variables for
only a small part of the sample.
In summary our results reinforce the view of the previous literature that ERPT
has been declining over time. We observed that this is true for all the economies in our
sample. However, with respect to the causes of the decrease in ERPT our results
provide only weak evidence in favour of the argument of Taylor (2000), Choudhri and
Hakura (2006), Gagnon and Ihrig (2004), Baqueiro, Diaz de Leon and Torres (2003)
and others, that advocates the importance of the lower inflation environment to reducing
ERPT. This hypothesis seems to be more appropriate for developed economies than for
emerging markets. In this sense, our results suggest that the correlations found in the
previous literature between inflation and ERPT did not mean exactly that a low inflation
regime is causing a low ERPT, but that a low ERPT is causing a lower inflation
environment. This finding highlights the importance of further econometric
investigation on the causes of the decline in ERPT observed in the countries that
adopted IT in the 1990s.
5. Conclusion
In this paper we have presented evidence on the role of low inflation on the decline of
the exchange rate pass-through (ERPT) for some developed and emerging market
economies that adopted Inflation Targeting (IT). Instead of just analysing correlations
between ERPT and inflation, we have provided evidence on temporal causality between
these variables.
We developed a state-space model of a Phillips curve, allowing for time
variation of the exchange rate parameter, and observed a gradual decline of the ERPT
12
after the adoption of IT. We modelled the ERPT as a function of its past value and
lagged inflation. We then applied likelihood ratio tests, which provided evidence of
causality between inflation and ERPT. For almost all the economies under consideration
we found that ERPT is an important variable in explaining the rate of inflation, but only
for few countries the causality also run the other way. In this sense further investigation
on this issue should be necessary in order to shed some light on the macroeconomic
determinants of ERPT.
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15
Table 1: Estimation period
Countries
Period
Australia
1981:1-2006:4 (1993:2)
Brazil
1995:1-2006:4 (1999:2)
Chile
1986:1-2006:4 (1991:1)
Czech Republic
1994:4-2006:4 (1998:1)
Hungary
1996:1-2006:4 (2001:2)
Iceland
1983:3-2006:4 (2001:1)
Israel
1990:1-2006:4 (1992:1)
Mexico
1980:3-2006:4 (1999:1)
New Zealand
1988:1-2006:4 (1990:1)
Norway
1981:1-2006:4 (2001:1)
Poland
1995:3-2006:4 (1998:4)
South Korea
1983:1-2006:4 (1998:2)
Switzerland
1980:4-2006:4 (2000:1)
Sweden
1981:1-2006:4 (1993:1)
Thailand
1993:3-2006:4 (2000:2)
United Kingdom
1980:3-2006:4 (1992:4)
Notes: the dates in parenthesis are the dates of adoption of
Inflation Targeting.
Table 2: State-space model estimation results
Australia
Brazil
Chile
Czech Republic
Hungary
Iceland
Mexico
New Zealand
Norway
Poland
Israel
South Korea
Switzerland
Sweden
Thailand
United Kingdom
Log Likelihood
-154.1082
-91.0541
-161.8151
-99.8528
-67.7258
-170.9993
-233.8218
-87.6329
-163.8045
-57.3341
-136.0843
-135.6297
-68.9071
-144.9307
-88.0339
-100.4403
SIC
3.4549
4.2778
4.4320
4.8864
4.1122
3.9283
4.7640
2.7050
3.6413
3.1403
4.4368
3.3827
1.6228
3.2784
3.8464
2.2031
Notes: Figures in the first column is the Log Likelihood of the
Maximum Likelihood estimation of the model. The second
column is the Schwarz Information Criterion (SIC) of the
estimated model.
16
Table 3: Granger Causality Tests
Australia
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Brazil
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Chile
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Czech Republic
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Hungary
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Iceland
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Mexico
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
New Zealand
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Norway
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Poland
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Israel
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
df
3
4
λ-stat.
3.001
17.932**
df
1
2
λ-stat.
0.044
5.220*
df
3
4
λ-stat.
9.529**
11.863**
df
2
3
λ-stat.
0.384
12.279**
df
3
4
λ-stat.
4.528
16.663**
df
1
2
λ-stat.
9.507**
2.776
df
1
2
λ-stat.
0.169
42.039**
df
1
2
λ-stat.
0.306
7.809**
df
3
4
λ-stat.
14.418**
34.701**
df
1
2
λ-stat.
5.036**
11.542**
df
1
2
λ-stat.
0.739
4.428*
17
South Korea
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Switzerland
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Sweden
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
Thailand
Inflation does not Granger Cause Pass-through
Pass-through does not Granger Cause Inflation
United Kingdom
df
3
4
λ-stat.
4.075
29.726**
df
1
2
λ-stat.
1.531
9.076**
df
3
4
λ-stat.
1.923
12.121**
df
1
2
λ-stat.
3.039*
19.500**
df
λ-stat.
Inflation does not Granger Cause Pass-through
1
2.516
Pass-through does not Granger Cause Inflation
2
20.313**
Notes: ** indicates significance at the 5% confidence level and * indicates
significance at the 10% confidence level. The λ-statistic follows the Chi-square
distribution with degrees of freedom equal to the number of restrictions imposed by
each null hypothesis.
18
Figure 1: Inflation and ERPT
Australia
Brazil
7
Inflation
Inflation
ERPT
ERPT
0.06
3.5
0.12
6
0.05
3.0
0.04
2.5
0.03
2.0
0.02
1.5
0.01
0.00
1.0
5
0.10
4
0.08
3
0.06
2
-0.01
0.5
1
-0.02
0.0
-0.03
0.04
0
-0.04
-0.5
0.02
-1
1980
1985
1990
1995
2000
1995
2005
2000
Chile
2005
Czech Republic
5
8
Inflation
Inflation
ERPT
0.25
ERPT
0.5
7
0.4
6
4
0.20
0.3
5
0.2
4
3
0.15
0.1
3
0.0
2
0.10
2
-0.1
1
1
0.05
0
0.00
-0.2
0
-0.3
-1
-0.4
1985
1990
1995
2000
1995
2005
2000
Hungary
Inflation
2005
Iceland
ERPT
Inflation
ERPT
0.5
5
0.15
20
0.4
4
0.10
15
0.3
3
10
0.05
0.2
2
5
0.00
0.1
1
0
-0.05
0.0
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1985
1990
Mexico
35
Inflation
1995
2000
2005
New Zealand
0.7
ERPT
30
0.6
3.5
Inflation
ERPT
0.150
3.0
0.125
2.5
25
0.5
0.100
2.0
20
0.4
1.5
15
0.075
0.3
1.0
0.050
10
0.2
0.5
5
0.025
0.1
0.0
0.0
-0.5
0
-5
1980
0.000
1985
1990
1995
2000
2005
1990
1995
2000
2005
19
Norway
Poland
4
0.3
Inflation
5
ERPT
Inflation
ERPT
0.125
0.2
3
4
0.100
0.1
2
0.0
3
0.075
1
-0.1
0
2
0.050
1
0.025
-0.2
-1
-0.3
-2
-0.4
1980
1985
1990
1995
2000
0.000
0
2005
1996
1997
1998
Israel
1999
2000
2001
2002
2003
2004
2005
2006
2007
South Korea
8
Inflation
ERPT
Inflation
0.25
ERPT
0.23
7
4
0.22
0.20
6
0.21
5
3
0.20
4
0.19
3
0.18
0.17
2
0.15
2
0.10
1
0.05
0.16
1
0.15
0
0.00
0
0.14
-1
-1
0.13
1990
1995
2000
-0.05
0.12
2005
1985
1990
1995
Switzerland
2000
2005
Sweden
2.5
4
Inflation
ERPT
0.075
Inflation
ERPT
0.075
3
2.0
0.050
1.5
0.050
2
1
1.0
0.025
0.025
0
0.5
0.000
0.000
0.0
1980
1985
1990
1995
2000
2005
-1
1980
1985
Thailand
Inflation
1990
1995
2000
2005
United Kingdom
ERPT
4.0
Inflation
0.03
ERPT
0.2
2.5
3.5
0.02
2.0
0.1
1.5
3.0
0.01
2.5
1.0
0.0
2.0
0.00
1.5
0.5
-0.1
0.0
1.0
-0.01
0.5
-0.5
-0.2
-0.02
0.0
-1.0
-0.5
1995
2000
2005
1980
1985
1990
1995
2000
2005
Notes: The graphs show the evolution of inflation (dashed line, left scale) and exchange rate pass-through
(black line, right scale).
20