ISSN 1518-3548
Working Paper Series
Nonlinear Mechanisms of the Exchange Rate Pass-Through:
A Phillips curve model with threshold for Brazil
Arnildo da Silva Correa and André Minella
November, 2006
ISSN 1518-3548
CGC 00.038.166/0001-05
Working Paper Series
Brasília
N. 122
Nov
2006
P. 1-30
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Nonlinear Mechanisms of the Exchange Rate Pass-Through:
A Phillips curve model with threshold for Brazil *
Arnildo da Silva Correa**
André Minella**
The Working Papers should not be reported as representing the views of the Banco Central do
Brasil. The views expressed in the papers are those of the author(s) and do not necessarily
reflect those of the Banco Central do Brasil.
Abstract
This paper investigates the presence of nonlinear mechanisms of pass-through
from the exchange rate to inflation in Brazil. In particular, it estimates a Phillips
curve with a threshold for the pass-through. The paper examines whether the
short-run magnitude of the pass-through is affected by the business cycle,
direction and magnitude of exchange rate changes, and exchange rate volatility.
The results indicate that the short-run pass-through is higher when the economy
is growing faster, when the exchange rate depreciates above some threshold and
when exchange rate volatility is lower. These results have important
implications for monetary policy and are possibly related to pricing-to-market
behavior, menu costs of price adjustment and uncertainty about the degree of
persistence in exchange rate movements.
Keywords: Exchange Rate Pass-Through, Threshold, Inflation, Nonlinearity,
Brazil
JEL Classification: E31, E50, E58
*
We are thankful to Fabio Araújo and Tomiê Sugahara for their participation in initial estimations, Carlos
Hamilton Araújo, Ana Beatriz Galvão and Marcelo Kfoury Muinhos for suggestions, Érica Oliveira and
Ibitisan Santos for assistance with data, and others colleagues at the Research Department of the Central Bank
of Brazil for their contributions and comments. The views herein are those of the authors and do not
necessarily reflect those of the Central Bank of Brazil.
**
Research Department, Central Bank of Brazil. E-mails: [email protected] and
[email protected]
3
1. Introduction
The presence of nonlinearities in the Phillips curve has relevant implications for
monetary policy. The slope of the Phillips curve – measuring the response of inflation to
output gap – affects directly the cost of disinflation. Schaling (2004) shows that, when the
Phillips curve is convex, that is, the sensitivity of inflation to economic activity increases
with the level of output, the optimal monetary policy reaction function is asymmetric.
Nonlinearity may also be present in the pass-through from exchange rate changes to prices.
If the pass-through, for instance, is greater when the economy is booming, we may consider
that the central bank's reaction to a depreciation of the domestic currency will be stronger in
this context.
In fact, the investigation of the presence of nonlinear mechanisms in the Phillips
curve has been an important topic in the recent literature.1 Most studies on nonlinear
Phillips curves for developed economies have focused on the slope of the Phillips curve and
the exchange rate pass-through. In the first case, Laxton, Rose and Tambakis (1999) and
Bean (2000) find evidence that the Phillips curve is convex, while Stiglitz (1997) and
Eisner (1997) claim that the Phillips curve is concave.2 The literature on exchange rate
pass-through, in turn, reports several sources of nonlinearity, indicating that the degree of
pass-through can be related to some macroeconomic variable, including the exchange rate.
Mann (1986), Goldberg (1995), Gil-Pareja (2000), Mahdavi (2002) and Olivei (2002) have
found asymmetric pass-through related to the direction of exchange rate changes, while
Ohno (1989) and Pollard and Coughlin (2004) have indicated the presence of asymmetry
associated with the magnitude of exchange rate changes.
The business cycle is also pointed out as an important source of nonlinearity for the
pass-through. The transmission of exchange rate depreciations to domestic prices would be
lower during an economic slowdown. Goldfajn and Werlang (2000), estimating a panel
data model for 71 countries, have found that depreciations have a higher pass-through to
prices when the economy is booming. In the case of Brazil, Carneiro, Monteiro and Wu
1
See, for instance, Chadha, Masson and Meredith (1992), Laxton, Meredith and Rose (1995), Dupasquier and
Ricketts (1998), Nobay and Pell (2000), Aguiar and Martins (2002), Tambakis (1999), and Clements and
Sensier (2003).
4
(2002) have found similar results estimating a backward-looking Phillips curve with the
pass-through coefficient as a function of unemployment rate and real exchange rate level.3
In these papers, the magnitude of the pass-through coefficient is a function of some
variables.
This paper investigates the possibility of a nonlinear pass-through in Brazil using
threshold models. These models are part of a class of models that consider different states
of nature or regimes and allow different dynamic behavior for the variables, conditional on
the regime prevailing in each moment (Franses and van Dijk, 2000). In the case of
threshold models, the sample is divided into classes based on whether the value of an
observed variable surpasses or not some threshold. This kind of model – Threshold
Autoregressive (TAR) Model – was initially proposed by Tong (1978) and Tong and Lim
(1980) and has spread in the recent applied economic literature.
We focus on the pass-through because exchange rate movements have played a key
role in the inflation dynamics in Brazil, especially in the early years of the inflation
targeting regime with recurrent bouts of exchange rate depreciation in response to shocks,
in the context of balance of payments vulnerabilities. We estimate three specifications for
the Phillips curve, which differ basically by the variable used as the threshold for the passthrough: i) output gap; ii) nominal exchange rate change; and iii) exchange rate volatility.
Thus, we can deal with different possible sources of nonlinearity. The first question is
whether economic activity affects the magnitude of the pass-through. The second one is
whether the pass-through is symmetric with respect to the direction of the exchange rate
change – whether appreciations or depreciations have symmetric effects on prices – and to
the magnitude of the exchange rate change.
The estimations indicate that the short-run pass-through is higher when the economy
is growing faster, when the exchange rate depreciates above a threshold value and when
exchange rate volatility is lower. These results have implications for monetary policy and
are possibly related to pricing-to-market behavior, costs of changing prices, and uncertainty
regarding the degree of persistence of exchange rate changes.
2
Filardo (1998), in turn, considers that the Phillips curve is neither entirely convex nor concave, but a
combination of both (a concave-convex curve).
3
Muinhos (2001) has found mixed results, and Bogdansky, Tombini and Werlang (2000) work with a model
for Brazil with a nonlinear pass-through as well.
5
The article is organized as follows. Section 2 sets forth the methodology of
threshold models with the presence of endogenous variables. Section 3 presents the
specifications of the Phillips curve with threshold and the estimation results. The last
section concludes the text.
2. Threshold models with endogenous variables
In threshold models, the sample is divided into classes based on the value of an
observed variable – whether it surpasses or not some threshold. As usual in practice, the
threshold is not known and needs to be estimated. The simplest model is the SETAR (SelfExciting Threshold Autoregressive Model), where the threshold is given by a lagged term
of the dependent variable – yt-d, where d>0. An AR(1) model of two regimes and d=1 can
be written as:
⎧ φ01 + φ11 yt −1 + ε t if yt −1 〈 τ
,
yt = ⎨ 2
2
⎩φ0 + φ1 yt −1 + ε t if yt −1 ≥ τ
(1)
where is the threshold value, φi j are the parameters i of the regime j, and ε t is an i.i.d.
white noise sequence conditional on the history of the series, denoted by
Ω t −1 = { yt −1 ,..., y t − p −1 , y t − p } , with zero mean and variance σ 2 . Alternatively, this model can
be expressed as:
yt = (φ 01 + φ11 yt −1 )[1 − I ( y t −1 〈 τ )] + (φ 02 + φ12 y t −1 ) I ( y t −1 ≥ τ ) + ε t ,
(2)
where I(.) is an indicator function that takes a value equal to either one or zero, depending
on the regime at time t.
For these models with exogenous regressors, there is a well-developed theory of
inference and estimation4. In the case of models with endogenous variables, in turn, the
theory is still working in progress. Caner and Hansen (2004) develop an estimator and an
4
See, for example, Chan (1993), Hansen (1996, 1999, 2000) and Caner (2002).
6
inference theory for this kind of model, with the restriction that the threshold variable must
be exogenous.
A model with endogenous regressors can be described as follows. Let { yt , z t , xt }tn=1
be the information set, where yt is unidimensional, z t is an m-dimension vector
(regressors), and xt is a k-dimension vector (instruments), with km. The threshold
variable qt = q( xt ) can be an element or a function of the vector xt. In a general way, the
structural equation can be written as:
⎧ yt = θ 1' zt + ζ t
⎨
'
⎩ yt = θ 2 zt + ζ t
qt 〈 τ
,
qt ≥ τ
(3)
or in a more compact way,
yt = θ1' zt [1 − I (qt 〈 τ )] + θ 2' zt I (qt ≥ τ ) + ζ t ,
(4)
where θ 'j are parameter vectors, τ ∈ Τ , and T is the set of the possible threshold values.
Since the error term ζ t is correlated with z t – at least one variable in vector z t is
endogenous – equation (4) cannot be estimated by ordinary least squares (OLS) because
parameters would be biased and inconsistent.
The method proposed by Caner and Hansen (2004) is based on the estimation of a
reduced form equation for the endogenous variables as a function of instrumental variables,
that is, a model with the conditional mean of the endogenous variables as a function of
exogenous variables. The estimated values are plugged into structural equation (4) and the
threshold value is estimated by minimizing the sum of the squared residuals. The
parameters of the structural equation are estimated in a third step, when the sample is
divided according to the estimated threshold. The estimation is conducted using the twostage least square method (2SLS) or the generalized method of moments (GMM).
Therefore, the first stage (conditional expectations model of z t ) is given by:
7
z t = f ( xt , β ) + ut ,
(5)
E (ut | xt ) = 0 ,
(6)
where β is a vector with parameters, ut is the error term, and f (.,.) is a function. In
particular, this function can also be conditioned on the threshold value (which can be equal
or different from that in the structural equation):5
f ( xt , β ) = ( β 1' xt )[1 − I (qt 〈τ )] + ( β 2' xt )[ I (qt ≥τ )] .
(7)
The parameter vector β in equation (5) can be obtained by OLS, for each ∈ T,
as:
⎞
⎛ n
βˆ1 (τ ) = ⎜⎜ ∑ xt xt' [ I (qt 〈τ )]⎟⎟
⎠
⎝ t =1
−1 n
⎛ n
⎞
βˆ 2 (τ ) = ⎜⎜ ∑ xt xt' [ I (qt ≥ τ )]⎟⎟
⎝ t =1
⎠
∑ xt zt' [ I (qt 〈τ )] ,
(8)
t =1
−1 n
∑ xt zt' [ I (qt ≥τ )] .
(9)
t =1
Using parameters β̂ , we can obtain the values ẑ t that will replace z t in the
structural equation. Doing it recursively for every ∈ T, the threshold value in the
structural equation can be chosen by the minimization of the sum of the squared residuals,
using a grid search. For every , let Y, ZL and ZG denote the vector yt and the matrices
z t [ I (qt 〈 τ )] e z t [ I (qt ≥τ )] , respectively. Thus, the threshold estimator is obtained from:
τˆ = arg min S n (τ ) ,
(10)
τ ∈T
where S n (τ ) is the sum of the squared residuals in the regression of Y on Ẑ L and Ẑ G . The
set of threshold values in (10) should be such that each regime has sufficient observations
5
In this paper, we do not condition on the threshold value in this first stage.
8
to generate reliable parameter estimation. According to Franses and van Dijk (1999), a safe
choice is at least 15% of the sample.
Given the estimated threshold value τˆ , the sample is divided into subsamples, and
parameters of equation (10) can be estimated by 2SLS as:
(
θˆ1 = ⎡ Zˆ L' Xˆ L Xˆ L' Xˆ L
⎢⎣
(
)
θˆ2 = ⎡ Zˆ G' Xˆ G Xˆ G' Xˆ G
⎢⎣
−1
)
(
−1
Xˆ L' Zˆ L ⎤ ⎡ Zˆ L' Xˆ L Xˆ L' Xˆ L
⎥⎦ ⎢⎣
−1
−1
(
)
Xˆ L' Y ⎤ ,
⎥⎦
(11)
)
(12)
−1
Xˆ G' Zˆ G ⎤ ⎡ Zˆ G' Xˆ G Xˆ G' Xˆ G
⎥⎦ ⎢⎣
−1
Xˆ G' Y ⎤ ,
⎥⎦
where Ẑ L , Ẑ G , X̂ L e X̂ G stand for the matrices with observations z t [ I (qt 〈τˆ)] ,
z t [ I (qt ≥τˆ)] , xt [ I (qt 〈 τˆ)] and xt [ I (qt ≥τˆ)] , respectively.
Caner and Hansen (2004) show that those estimators are consistent, although not
necessarily efficient. Their applicability is conditioned on the exogeneity of the threshold
variable.
3. Phillips curve model for Brazil
Aiming to test the possibility of the presence of a nonlinear pass-through from the
exchange rate to inflation, we estimate some Phillips curve models for Brazil combined
with the methodology of regime switching described in the previous section.
The estimated Phillips curve relates inflation to a measure of real disequilibrium
(output gap), inflation expectations, past inflation, exchange rate changes and external
inflation, with a threshold variable:
⎧π tL = α11Etπ t +1 + (1 − α11 − α 21 )π t −1 + α 21 (Δet −1 + π t*−1 ) + α 41 yt −1 + ε t
⎨ L
2
2
2
2
*
2
⎩ π t = α1 Etπ t +1 + (1 − α1 − α 2 )π t −1 + α 2 (Δet −1 + π t −1 ) + α 4 yt −1 + ε t
if qt 〈 τ
,
if qt ≥τ
(13)
where α i j is the parameter of a specific regressor i when the economy is in regime j, π tL is
free IPCA inflation (headline inflation measured by the Broad National Consumer Price
9
Index, but excluding administered prices), π t is headline IPCA inflation, π t* is a measure
of external inflation (PPI in the U.S.), y t is output gap (actual minus potential output)6, et
is the natural logarithm of the average nominal exchange rate (domestic currency units per
dollar), Et(.) is the expectations operator conditional on the information available at t, Δ is
the difference operator ( Δet −1 = et −1 − et − 2 ), ε t is a residual, qt is the threshold variable,
and τ ∈T, where T is the set of possible values for qt .
The dependent variable is the “free prices” component of headline inflation because
administered prices have a different price dynamics, partially obeying contract rules. Note
that the estimated pass-through refers to the transmission from exchange rate change in the
previous quarter to the current inflation, that is, it captures only the short-run effect of
exchange rate movements.
To enable the joint estimation, the previous equations become:
(
)
)(I [q ≥τ )+ ε
π tL = α11Etπ t +1 + (1 − α11 − α 21 )π t −1 + α 21 (Δet −1 + π t*−1 ) + α 41 yt −1 (1 − I [qt 〈τ ) +
(α
2
1
Etπ t +1 + (1 − α12 − α 22 )π t −1 + α 22 (Δet −1 + π t*−1 ) + α 42 yt −1
t
.
(14)
t
Based on theoretical indications for a nonlinear exchange rate pass-through, we
evaluate three threshold variables: i) business cycle, measured by the output gap; ii)
magnitude of nominal exchange rate changes; and iii) a measure of exchange rate volatility.
We use quarterly data from 1995:1 through 2005:4 and estimate using 2SLS, with
instrumental variables for the inflation expectations term.
The first estimated specification has the output gap as the threshold variable. In this
model, all parameters, except for that of the output gap, are subject to regime switching.7
The estimation results are the following (p-values in parentheses):
6
The output gap used in the estimation was generated using a production function model. See, for example,
the box "Methodologies for estimating the potential output" in Banco Central do Brasil (2003) and Muinhos
and Alves (2003) for a description of the methodology.
7
We do not allow the coefficient on output gap to change because we want to capture nonlinearities in the
pass-through.
10
π tL = 0.75Et π t +1 + 0.25π t −1 + 0.00(Δet −1 + π*t −1 ) + 0.24 yt − 2
(0.00)
(0.23)
(0.78)
( 0.00)
π tL = 0.58Et π t +1 + 0.33π t −1 + 0.09(Δet −1 + π*t −1 ) + 0.24 y t − 2
(0.02)
(0.11)
if yt −1 〈 − 1.89%
(0.03)
if y t −1 ≥ −1.89%
( 0.00)
Sample period: 1995:1–2005:4
Standard errors estimated using Newey-West consistent estimators
P-values in parentheses
Impulse dummy variable for 1999:1: 0.02, p-value: 0.00
Instrumental variables: const., dummy variable, π t −1 , π t − 2 , Δet −1 , Δet − 2 , π t −1 , π t − 2 , y t − 2
R-squared: 0.50
Adjusted R-squared: 0.43
Breusch-Godfrey Serial Correlation LM test, p-values: 1 lag: 0.54, 4 lags: 0.33
White Heteroskedasticity Test, p-value: 0.27
Jarque-Bera Normality Test, p-value: 0.57
*
*
Wald test α 2 = α 2 , p-value: 0.04
1
2
According to the estimation results, there is a nonlinearity in the pass-through term
related to the business cycle: the exchange rate pass-through is not statistically different
from zero in the regime when the economy is below the threshold, whereas it is around 9%
when economic activity is higher. According to the Wald test, we reject the null that the
two pass-through coefficients are equal. The exchange rate pass-through is significantly
greater when output is above some threshold8, estimated at 1.89% below the potential
output. This means that, during an economic slowdown, an exchange rate variation will
have a smaller impact on domestic prices. This is usually pointed as one of the factors that
limited the pass-through in Brazil during the 1999 exchange rate crisis. One limitation of
this result is its implication that exchange rate appreciations have a higher pass-through
when the economy is booming than when the output gap is below the threshold.
The other parameter estimates are in line with those found in the literature using
models without a threshold variable. Except for the backward-looking term, all coefficients
are statistically significant at 5%. In addition, the estimated value for the threshold splits the
sample into two approximately equal parts (19 observations when y t −1 < −1.89 %, and 25
when y t −1 ≥ −1.89 %). This means that, although the sample size is not large, none of the
8
This result is in line with those in Goldfajn and Werlang (2000), and Carneiro, Monteiro and Wu (2002).
11
regimes was estimated with an extremely low number of observations.9 Actually, we have
tried several specifications, using different instruments for the expectations term, and the
results were robust.
The second estimated model considers nominal exchange rate changes as the
threshold variable. Similarly to the previous model, all parameters are allowed to vary with
the regime change, except for the output gap parameter, kept constant in both regimes. The
estimation results are the following10:
π tL = 0.58Et π t +1 + 0.40π t −1 + 0.02(Δet −1 + π*t −1 ) + 0.27 yt −1
(0.00)
(0.00)
(0.31)
if Δet −1 〈 2.10%
( 0.01)
π tL = 0.44 Et π t +1 + 0.45π t −1 + 0.11(Δet −1 + π*t −1 ) + 0.27 y t −1
(0.10)
(0.07)
(0.03)
if Δet −1 ≥ 2.10%
( 0.01)
.
Sample period: 1995:1–2005:4
Standard errors estimated using Newey-West consistent estimators
P-values in parentheses
Impulse dummy variable for 1999:1: 0.03, p-value: 0.08
Instrument variables: const., dummy var., π t −1 , π t − 2 , π t −3 , Δet −1 , Δet − 2 , π t −1 , π t − 2 , y t − 2
R-squared: 0.50
Adjusted R-squared: 0.43
Breusch-Godfrey Serial Correlation LM test, p-values: 1 lag: 1.00, 4 lags: 0.38
White Heteroskedasticity Test, p-value: 0.69
Jarque-Bera Normality Test, p-value: 0.31
*
*
Wald test α 2 = α 2 , p-value: 0.06
1
2
Those results indicate that the short-run effect of exchange rate changes on inflation
is asymmetric. In the case of large exchange rate depreciations, the estimated pass-through
for the following quarter is around 11%, whereas appreciations or small depreciations do
not have a statistically significant effect. The Wald test rejects the null hypothesis that both
coefficients are equal. Therefore, the pass-through is greater when quarter-on-quarter
depreciations are equal to or larger than 2.1%. Although the results on the effect of an
9
Including the period previous to the launch of the Real Plan is not recommendable because the inflation
dynamics in a high inflation regime is substantially different, distorting the estimation.
10
In that specification, we have used for the backward-looking inflation and output gap terms the average of
(
)
(
)
their values at t-1 and t-2, that is, π t −1 = π A + π A
/ 2 and y t −1 = y A + y A
/ 2 , where the
t −1
t−2
t −1
t−2
superscript A means actual values. That specification generates better fitting.
12
appreciation in the previous quarter on current inflation were not statistically significant,
we should not infer that appreciations are not transmitted to prices. This transmission can
take place with more lags than in the case of depreciation.
As before, the estimated parameters are robust with respect to the instruments used
and are statistically significant at 5% (except the coefficient on lagged inflation when
Δet −1 ≥ 2.10% ). Moreover, the number of observations in each regime was reasonably
balanced (15 observations in the large depreciation regime, and 29 in the other) and the
estimated values for the coefficients are close to those reported in the literature. Note that in
both estimations the forward-looking inflation coefficient is greater than the backwardlooking component.
Since the estimated threshold is not zero, its slightly positive value (close to 2%)
suggests the presence of menu costs or some adjustment costs of prices, where small
exchange rate changes are not promptly transmitted to prices. If price changes are costly, a
small change in the currency value can be accommodated within the markup margin. In this
case, firms tend to postpone their decisions, adjusting their markup in the short-run.
However, if exchange rate changes surpass some limit, even if the change is temporary, the
costs of not adjusting prices are higher, leading firms to change prices more rapidly.
Consequently, the presence of menu costs increases the possibility that firms will adjust
price mainly if exchange rate changes surpass some threshold, resulting in an asymmetric
pass-through related to small and large exchange rate changes.
Furthermore, pricing-to-market theory delivers an explanation for a partial passthrough and for an asymmetry related to appreciations and depreciations in the exchange
rate. Consider the domestic sector formed by subsidiaries of foreign firms that produce
abroad and sell their products internally. In the case of an exchange rate depreciation, those
firms have three options: i) to reduce their markup to keep stable the price in local currency
(absence of pass-through); ii) to keep their markup, increasing the price charged in local
currency to reflect completely the exchange rate change (complete pass-through), which
may imply a market share reduction; or iii) a combination of the previous two possibilities
(partial pass-through).
When subsidiary firms are trying to build up or keep their market shares, a local
currency depreciation results in a lower pass-through than that when there is an
13
appreciation. Nevertheless, if the depreciation of the domestic currency is high, there is less
room for markup adjustments, and at least partially the depreciation is transmitted to
domestic prices to avoid losses. In the case of appreciation, firms' profits increase if they
keep constant domestic prices, which could result in a longer period to adjust prices
downwards. The extension of these effects on the price level in the economy depends on
the price-elasticity of demand for these firms’ goods and on the degree of openness of the
economy. In addition, if the firms that produce abroad face a restriction on their production
capacity, an exchange rate appreciation can result in a lower pass-through than in the case
of a depreciation. The restriction capacity limits the fall in domestic price that the
appreciation could generate.
The previous estimated models do not make any distinction between the pre-1999
period, when the exchange rate was managed – following in practice a crawling peg system
– and the following period of a floating rate. In fact, when we include a step dummy into
the exchange rate term, the results deteriorate substantially in terms of signs and statistical
significance of the parameters. This result may be related to the increase in the number of
parameters to be estimated when we include a dummy variable, reducing the degree of
freedom of the estimation.
Because of those limitations, we have estimated a third model, using exchange rate
volatility as the threshold variable. This estimation would tend to resolve, at least partially,
the problem of the separation of the exchange rate regimes (before and after January 1999)
because the threshold estimation tends to classify the observations of the managed system
period into the low volatility regime. In principle, the low volatility estimated regime could
also contain observations from when the exchange rate was relatively stable during the float
period.
In addition, that estimation aims to capture the inflationary effects in two different
situations: i) when agents perceive exchange rate changes as transitory; and ii) when they
perceive them as permanent. When agents consider exchange rate variations as more
permanent, more promptly they will be transmitted to prices. Our assumption is that the
probability that agents consider changes as permanent is higher in periods of low exchange
14
rate volatility and smaller in periods of greater volatility.11 Thus, we would expect a lower
pass-through in periods of higher exchange rate instability.
We have used the standard deviation of daily changes in the exchange rate within
each quarter as the measure of volatility. The estimation results were the following12:
π tL = 0.15 Etπ t +1 + 0.05π t −1 + 0.80(Δet −1 + π t*−1 ) + 0.31yt −1
(0.74)
(0.83)
(0.21)
( 0.00)
π tL = 0.30 Etπ t +1 + 0.63π t −1 + 0.07(Δet −1 + π t*−1 ) + 0.31yt −1
(0.07)
(0.00)
if σ e,t −1 〈 0.07%
(0.05)
if σ e,t −1 ≥ 0.07%
( 0.00)
.
Sample period: 1995:1–2005:4
Standard errors estimated using Newey-West consistent estimators
P-values in parentheses
Impulse dummy variable for 1999:1: 0.03, p-value: 0.00
Instrumental variables: const., dummy variable, π t −1 , π t − 2 , Δet −1 , Δet − 2 , π t −1 , π t − 2 , yt −1
R-squared: 0.52
Adjusted R-squared: 0.45
Breusch-Godfrey Serial Correlation LM test, p-values: 1 lag: 0.69, 4 lags: 0.22
White Heteroskedasticity Test, p-value: 0.31
Jarque-Bera Normality Test, p-value: 0.21
*
*
Wald test α 2 = α 2 , p-value: 0.25
1
2
In terms of magnitude, the point estimates indicate a greater pass-through in low
volatility periods than in high volatility moments (80% and 7%, respectively). However,
the estimated pass-through is not statistically significant in the low volatility regime,
although it is significant in the other regime and the parameter values are close to those
reported in the literature for the periods of managed and floating exchange rates.13 The
resulting sample division assigned most of the observations of the managed system to the
11
Albuquerque and Portugal (2006), for example, explore the relationship between exchange rate volatility
and inflation in Brazil, using a bivariate GARCH model.
12
In that specification, we have used for the backward-looking inflation term the average of their values at t-
(
)
1, t-2 and t-3, that is, π t −1 = π A + π A + π A
/ 3 , and for the output gap term, the average at t-1 and tt −1
t−2
t −3
(
)
2, that is, y t −1 = y A + y A
/ 2 , where the superscript A means actual values. That specification has
t −1
t−2
generated better fitting.
15
low volatility regime. The observations corresponding to values below the threshold
comprise the 1995:4–1998:2 period. Nevertheless, according to the Wald test, we cannot
reject the null that both coefficients are equal, and the results of this Phillips curve
specification are less robust than those of the two previous models. Therefore, these results
should be considered with more caution.
Figure 1 illustrates the results concerning the first model estimated. It presents the
quarterly free price inflation, the exchange rate change (lagged one period), and a line that
indicates the threshold value (-1.89%) of output gap. This line separates the periods when
output gap is above and below the threshold. We point out some periods, described in Table
1, in which the estimated model can explain, at least partially, the relation between
exchange rate changes and free price inflation. The table records the corresponding values,
besides including headline inflation (measured by IPCA).
Figure 1 – Free price inflation, exchange rate changes and the output gap
7
40
Free price inflation (%)
5
25
4
3
10
2
1
-5
0
–
d+
d
-1
d
+
d
+
a+
-20
Exchange rate change (%)
6
-2
-3
-35
I
II III IV I
1998
II
III IV I
1999
Free price inflation
II III IV I
II III IV I
II III IV I
2000
2001
2002
2003
2004
2005
Period output gap above the thr eshol d
Period output gap bel ow the threshold
II III IV I
II III IV I
II III
Exc hange rate change (t-1)
13
Muinhos and Alves (2003), por instance, have found a coefficient reduction from 51% to 6% after the
change in the exchange rate regime, and Albuquerque and Portugal (2005), using a Kalman filter model, have
estimated parameters values around 42% and 4%, respectively.
16
Table 1 – Inflation and exchange rate changes in selected periods
Period
1999: II
2000:III
2001:II
2001:III
2002:III
2002:IV
2004:IV
2005:I
2005:II
Characteristic
–
d
+
d
+
d
+
d
+
d
+
d
+
a
+
a
+
a
Headline
inflation
in t-1
2.88
0.66
1.42
1.52
1.44
2.58
1.94
2.00
1.79
Headline
inflation
in t
1.05
3.18
1.52
2.33
2.58
6.56
2.00
1.79
1.34
Free price
inflation
in t-1
2.59
0.71
1.42
1.40
0.62
2.56
1.35
1.31
1.72
Free price
inflation
in t
0.49
1.93
1.40
1.41
2.56
6.34
1.31
1.72
1.40
Exchange
rate change
in t-1
39.33
1.64
4.39
12.69
4.87
22.30
-2.30
-6.80
-4.42
+
Note: d means depreciation with booming
d– means depreciation with recession
+
a means appreciation with booming
–
a means appreciation with recession
The exchange rate change is calculated based on the quarterly exchange rate average
In the second quarter of 1999 (immediately after the float), for instance, in spite of
the 39% exchange rate depreciation in the previous quarter, free price inflation was only
0.49% and the headline inflation stood at 1.05%, both below the previous quarter values. In
that period, the output gap was below the estimated threshold (economic slowdown), which
implies, according to the model, a low pass-through to inflation. The depreciations in the
third quarter of 2000 and during 2001, in turn, were followed by higher increases in the
inflation rate. In that period, the output gap was higher than the estimated threshold.
In mid-2002, when the economy was growing faster, a strong depreciation was
accompanied by a great inflation rise. In the last quarter, for instance, when the
depreciation in the previous quarter reached 22%, free price inflation went from 2.56% to
6.34%, and headline inflation rose from 2.58% to 6.56%.
On the other hand, although inflation fell along 2005, it did not follow so promptly
the exchange rate appreciation started in the last quarter of 2004. One possible explanation
lies on the asymmetry of the short-run pass-through with respect to appreciation and
depreciation, put in evidence by the model with the threshold given by exchange rate
changes. Furthermore, initial movements of appreciation were possibly not perceived
immediately as having longer duration, postponing the effect on prices. In fact, the model
estimates the short-run pass-through, that is, the effect on current inflation of the change in
17
the exchange rate in the previous quarter. The appreciation contributed for the reduction in
inflation, but probably with lags greater than one quarter.
4. Conclusions
This paper explores the possibility of the presence of a nonlinear pass-through from
the exchange rate to inflation in Brazil. We have estimated models for the Phillips curve
combined with the methodology of threshold models. In these models, the parameter values
depend on which regime the economy is, which are determined endogenously by means of
an observed variable.
The choice of variables used was based on the possible sources of nonlinearity of
the pass-through reported in the literature. In particular, we have examined three sources: i)
business cycle; ii) exchange rate changes; and iii) exchange rate volatility.
The estimations indicate the presence of nonlinear mechanisms in the short-run
pass-through in Brazil. The short-run pass-through is higher when the economy is booming,
when the exchange rate depreciates above some threshold, and when exchange rate
volatility is lower. These results have important implications for monetary policy and are
possibly related to a pricing-to-market behavior, menu costs to change prices, and
uncertainty about the degree of persistence of exchange rate changes.
18
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21
Banco Central do Brasil
Trabalhos para Discussão
Os Trabalhos para Discussão podem ser acessados na internet, no formato PDF,
no endereço: http://www.bc.gov.br
Working Paper Series
Working Papers in PDF format can be downloaded from: http://www.bc.gov.br
1
Implementing Inflation Targeting in Brazil
Joel Bogdanski, Alexandre Antonio Tombini and Sérgio Ribeiro da Costa
Werlang
Jul/2000
2
Política Monetária e Supervisão do Sistema Financeiro Nacional no
Banco Central do Brasil
Eduardo Lundberg
Jul/2000
Monetary Policy and Banking Supervision Functions on the Central
Bank
Eduardo Lundberg
Jul/2000
3
Private Sector Participation: a Theoretical Justification of the Brazilian
Position
Sérgio Ribeiro da Costa Werlang
Jul/2000
4
An Information Theory Approach to the Aggregation of Log-Linear
Models
Pedro H. Albuquerque
Jul/2000
5
The Pass-Through from Depreciation to Inflation: a Panel Study
Ilan Goldfajn and Sérgio Ribeiro da Costa Werlang
Jul/2000
6
Optimal Interest Rate Rules in Inflation Targeting Frameworks
José Alvaro Rodrigues Neto, Fabio Araújo and Marta Baltar J. Moreira
Jul/2000
7
Leading Indicators of Inflation for Brazil
Marcelle Chauvet
Sep/2000
8
The Correlation Matrix of the Brazilian Central Bank’s Standard Model
for Interest Rate Market Risk
José Alvaro Rodrigues Neto
Sep/2000
9
Estimating Exchange Market Pressure and Intervention Activity
Emanuel-Werner Kohlscheen
Nov/2000
10
Análise do Financiamento Externo a uma Pequena Economia
Aplicação da Teoria do Prêmio Monetário ao Caso Brasileiro: 1991–1998
Carlos Hamilton Vasconcelos Araújo e Renato Galvão Flôres Júnior
Mar/2001
11
A Note on the Efficient Estimation of Inflation in Brazil
Michael F. Bryan and Stephen G. Cecchetti
Mar/2001
12
A Test of Competition in Brazilian Banking
Márcio I. Nakane
Mar/2001
22
13
Modelos de Previsão de Insolvência Bancária no Brasil
Marcio Magalhães Janot
Mar/2001
14
Evaluating Core Inflation Measures for Brazil
Francisco Marcos Rodrigues Figueiredo
Mar/2001
15
Is It Worth Tracking Dollar/Real Implied Volatility?
Sandro Canesso de Andrade and Benjamin Miranda Tabak
Mar/2001
16
Avaliação das Projeções do Modelo Estrutural do Banco Central do
Brasil para a Taxa de Variação do IPCA
Sergio Afonso Lago Alves
Mar/2001
Evaluation of the Central Bank of Brazil Structural Model’s Inflation
Forecasts in an Inflation Targeting Framework
Sergio Afonso Lago Alves
Jul/2001
Estimando o Produto Potencial Brasileiro: uma Abordagem de Função
de Produção
Tito Nícias Teixeira da Silva Filho
Abr/2001
Estimating Brazilian Potential Output: a Production Function Approach
Tito Nícias Teixeira da Silva Filho
Aug/2002
18
A Simple Model for Inflation Targeting in Brazil
Paulo Springer de Freitas and Marcelo Kfoury Muinhos
Apr/2001
19
Uncovered Interest Parity with Fundamentals: a Brazilian Exchange
Rate Forecast Model
Marcelo Kfoury Muinhos, Paulo Springer de Freitas and Fabio Araújo
May/2001
20
Credit Channel without the LM Curve
Victorio Y. T. Chu and Márcio I. Nakane
May/2001
21
Os Impactos Econômicos da CPMF: Teoria e Evidência
Pedro H. Albuquerque
Jun/2001
22
Decentralized Portfolio Management
Paulo Coutinho and Benjamin Miranda Tabak
Jun/2001
23
Os Efeitos da CPMF sobre a Intermediação Financeira
Sérgio Mikio Koyama e Márcio I. Nakane
Jul/2001
24
Inflation Targeting in Brazil: Shocks, Backward-Looking Prices, and
IMF Conditionality
Joel Bogdanski, Paulo Springer de Freitas, Ilan Goldfajn and
Alexandre Antonio Tombini
Aug/2001
25
Inflation Targeting in Brazil: Reviewing Two Years of Monetary Policy
1999/00
Pedro Fachada
Aug/2001
26
Inflation Targeting in an Open Financially Integrated Emerging
Economy: the Case of Brazil
Marcelo Kfoury Muinhos
Aug/2001
27
Complementaridade e Fungibilidade dos Fluxos de Capitais
Internacionais
Carlos Hamilton Vasconcelos Araújo e Renato Galvão Flôres Júnior
Set/2001
17
23
28
Regras Monetárias e Dinâmica Macroeconômica no Brasil: uma
Abordagem de Expectativas Racionais
Marco Antonio Bonomo e Ricardo D. Brito
Nov/2001
29
Using a Money Demand Model to Evaluate Monetary Policies in Brazil
Pedro H. Albuquerque and Solange Gouvêa
Nov/2001
30
Testing the Expectations Hypothesis in the Brazilian Term Structure of
Interest Rates
Benjamin Miranda Tabak and Sandro Canesso de Andrade
Nov/2001
31
Algumas Considerações sobre a Sazonalidade no IPCA
Francisco Marcos R. Figueiredo e Roberta Blass Staub
Nov/2001
32
Crises Cambiais e Ataques Especulativos no Brasil
Mauro Costa Miranda
Nov/2001
33
Monetary Policy and Inflation in Brazil (1975-2000): a VAR Estimation
André Minella
Nov/2001
34
Constrained Discretion and Collective Action Problems: Reflections on
the Resolution of International Financial Crises
Arminio Fraga and Daniel Luiz Gleizer
Nov/2001
35
Uma Definição Operacional de Estabilidade de Preços
Tito Nícias Teixeira da Silva Filho
Dez/2001
36
Can Emerging Markets Float? Should They Inflation Target?
Barry Eichengreen
Feb/2002
37
Monetary Policy in Brazil: Remarks on the Inflation Targeting Regime,
Public Debt Management and Open Market Operations
Luiz Fernando Figueiredo, Pedro Fachada and Sérgio Goldenstein
Mar/2002
38
Volatilidade Implícita e Antecipação de Eventos de Stress: um Teste para
o Mercado Brasileiro
Frederico Pechir Gomes
Mar/2002
39
Opções sobre Dólar Comercial e Expectativas a Respeito do
Comportamento da Taxa de Câmbio
Paulo Castor de Castro
Mar/2002
40
Speculative Attacks on Debts, Dollarization and Optimum Currency
Areas
Aloisio Araujo and Márcia Leon
Apr/2002
41
Mudanças de Regime no Câmbio Brasileiro
Carlos Hamilton V. Araújo e Getúlio B. da Silveira Filho
Jun/2002
42
Modelo Estrutural com Setor Externo: Endogenização do Prêmio de
Risco e do Câmbio
Marcelo Kfoury Muinhos, Sérgio Afonso Lago Alves e Gil Riella
Jun/2002
43
The Effects of the Brazilian ADRs Program on Domestic Market
Efficiency
Benjamin Miranda Tabak and Eduardo José Araújo Lima
Jun/2002
24
Jun/2002
44
Estrutura Competitiva, Produtividade Industrial e Liberação Comercial
no Brasil
Pedro Cavalcanti Ferreira e Osmani Teixeira de Carvalho Guillén
45
Optimal Monetary Policy, Gains from Commitment, and Inflation
Persistence
André Minella
Aug/2002
46
The Determinants of Bank Interest Spread in Brazil
Tarsila Segalla Afanasieff, Priscilla Maria Villa Lhacer and Márcio I. Nakane
Aug/2002
47
Indicadores Derivados de Agregados Monetários
Fernando de Aquino Fonseca Neto e José Albuquerque Júnior
Set/2002
48
Should Government Smooth Exchange Rate Risk?
Ilan Goldfajn and Marcos Antonio Silveira
Sep/2002
49
Desenvolvimento do Sistema Financeiro e Crescimento Econômico no
Brasil: Evidências de Causalidade
Orlando Carneiro de Matos
Set/2002
50
Macroeconomic Coordination and Inflation Targeting in a Two-Country
Model
Eui Jung Chang, Marcelo Kfoury Muinhos and Joanílio Rodolpho Teixeira
Sep/2002
51
Credit Channel with Sovereign Credit Risk: an Empirical Test
Victorio Yi Tson Chu
Sep/2002
52
Generalized Hyperbolic Distributions and Brazilian Data
José Fajardo and Aquiles Farias
Sep/2002
53
Inflation Targeting in Brazil: Lessons and Challenges
André Minella, Paulo Springer de Freitas, Ilan Goldfajn and
Marcelo Kfoury Muinhos
Nov/2002
54
Stock Returns and Volatility
Benjamin Miranda Tabak and Solange Maria Guerra
Nov/2002
55
Componentes de Curto e Longo Prazo das Taxas de Juros no Brasil
Carlos Hamilton Vasconcelos Araújo e Osmani Teixeira de Carvalho de
Guillén
Nov/2002
56
Causality and Cointegration in Stock Markets:
the Case of Latin America
Benjamin Miranda Tabak and Eduardo José Araújo Lima
Dec/2002
57
As Leis de Falência: uma Abordagem Econômica
Aloisio Araujo
Dez/2002
58
The Random Walk Hypothesis and the Behavior of Foreign Capital
Portfolio Flows: the Brazilian Stock Market Case
Benjamin Miranda Tabak
Dec/2002
59
Os Preços Administrados e a Inflação no Brasil
Francisco Marcos R. Figueiredo e Thaís Porto Ferreira
Dez/2002
60
Delegated Portfolio Management
Paulo Coutinho and Benjamin Miranda Tabak
Dec/2002
25
61
O Uso de Dados de Alta Freqüência na Estimação da Volatilidade e
do Valor em Risco para o Ibovespa
João Maurício de Souza Moreira e Eduardo Facó Lemgruber
Dez/2002
62
Taxa de Juros e Concentração Bancária no Brasil
Eduardo Kiyoshi Tonooka e Sérgio Mikio Koyama
Fev/2003
63
Optimal Monetary Rules: the Case of Brazil
Charles Lima de Almeida, Marco Aurélio Peres, Geraldo da Silva e Souza
and Benjamin Miranda Tabak
Feb/2003
64
Medium-Size Macroeconomic Model for the Brazilian Economy
Marcelo Kfoury Muinhos and Sergio Afonso Lago Alves
Feb/2003
65
On the Information Content of Oil Future Prices
Benjamin Miranda Tabak
Feb/2003
66
A Taxa de Juros de Equilíbrio: uma Abordagem Múltipla
Pedro Calhman de Miranda e Marcelo Kfoury Muinhos
Fev/2003
67
Avaliação de Métodos de Cálculo de Exigência de Capital para Risco de
Mercado de Carteiras de Ações no Brasil
Gustavo S. Araújo, João Maurício S. Moreira e Ricardo S. Maia Clemente
Fev/2003
68
Real Balances in the Utility Function: Evidence for Brazil
Leonardo Soriano de Alencar and Márcio I. Nakane
Feb/2003
69
r-filters: a Hodrick-Prescott Filter Generalization
Fabio Araújo, Marta Baltar Moreira Areosa and José Alvaro Rodrigues Neto
Feb/2003
70
Monetary Policy Surprises and the Brazilian Term Structure of Interest
Rates
Benjamin Miranda Tabak
Feb/2003
71
On Shadow-Prices of Banks in Real-Time Gross Settlement Systems
Rodrigo Penaloza
Apr/2003
72
O Prêmio pela Maturidade na Estrutura a Termo das Taxas de Juros
Brasileiras
Ricardo Dias de Oliveira Brito, Angelo J. Mont'Alverne Duarte e Osmani
Teixeira de C. Guillen
Maio/2003
73
Análise de Componentes Principais de Dados Funcionais – Uma
Aplicação às Estruturas a Termo de Taxas de Juros
Getúlio Borges da Silveira e Octavio Bessada
Maio/2003
74
Aplicação do Modelo de Black, Derman & Toy à Precificação de Opções
Sobre Títulos de Renda Fixa
Octavio Manuel Bessada Lion, Carlos Alberto Nunes Cosenza e César das
Neves
Maio/2003
75
Brazil’s Financial System: Resilience to Shocks, no Currency
Substitution, but Struggling to Promote Growth
Ilan Goldfajn, Katherine Hennings and Helio Mori
26
Jun/2003
76
Inflation Targeting in Emerging Market Economies
Arminio Fraga, Ilan Goldfajn and André Minella
Jun/2003
77
Inflation Targeting in Brazil: Constructing Credibility under Exchange
Rate Volatility
André Minella, Paulo Springer de Freitas, Ilan Goldfajn and Marcelo Kfoury
Muinhos
Jul/2003
78
Contornando os Pressupostos de Black & Scholes: Aplicação do Modelo
de Precificação de Opções de Duan no Mercado Brasileiro
Gustavo Silva Araújo, Claudio Henrique da Silveira Barbedo, Antonio
Carlos Figueiredo, Eduardo Facó Lemgruber
Out/2003
79
Inclusão do Decaimento Temporal na Metodologia
Delta-Gama para o Cálculo do VaR de Carteiras
Compradas em Opções no Brasil
Claudio Henrique da Silveira Barbedo, Gustavo Silva Araújo,
Eduardo Facó Lemgruber
Out/2003
80
Diferenças e Semelhanças entre Países da América Latina:
uma Análise de Markov Switching para os Ciclos Econômicos
de Brasil e Argentina
Arnildo da Silva Correa
Out/2003
81
Bank Competition, Agency Costs and the Performance of the
Monetary Policy
Leonardo Soriano de Alencar and Márcio I. Nakane
Jan/2004
82
Carteiras de Opções: Avaliação de Metodologias de Exigência de Capital
no Mercado Brasileiro
Cláudio Henrique da Silveira Barbedo e Gustavo Silva Araújo
Mar/2004
83
Does Inflation Targeting Reduce Inflation? An Analysis for the OECD
Industrial Countries
Thomas Y. Wu
May/2004
84
Speculative Attacks on Debts and Optimum Currency Area: a Welfare
Analysis
Aloisio Araujo and Marcia Leon
May/2004
85
Risk Premia for Emerging Markets Bonds: Evidence from Brazilian
Government Debt, 1996-2002
André Soares Loureiro and Fernando de Holanda Barbosa
May/2004
86
Identificação do Fator Estocástico de Descontos e Algumas Implicações
sobre Testes de Modelos de Consumo
Fabio Araujo e João Victor Issler
Maio/2004
87
Mercado de Crédito: uma Análise Econométrica dos Volumes de Crédito
Total e Habitacional no Brasil
Ana Carla Abrão Costa
Dez/2004
88
Ciclos Internacionais de Negócios: uma Análise de Mudança de Regime
Markoviano para Brasil, Argentina e Estados Unidos
Arnildo da Silva Correa e Ronald Otto Hillbrecht
Dez/2004
89
O Mercado de Hedge Cambial no Brasil: Reação das Instituições
Financeiras a Intervenções do Banco Central
Fernando N. de Oliveira
Dez/2004
27
90
Bank Privatization and Productivity: Evidence for Brazil
Márcio I. Nakane and Daniela B. Weintraub
Dec/2004
91
Credit Risk Measurement and the Regulation of Bank Capital and
Provision Requirements in Brazil – A Corporate Analysis
Ricardo Schechtman, Valéria Salomão Garcia, Sergio Mikio Koyama and
Guilherme Cronemberger Parente
Dec/2004
92
Steady-State Analysis of an Open Economy General Equilibrium Model
for Brazil
Mirta Noemi Sataka Bugarin, Roberto de Goes Ellery Jr., Victor Gomes
Silva, Marcelo Kfoury Muinhos
Apr/2005
93
Avaliação de Modelos de Cálculo de Exigência de Capital para Risco
Cambial
Claudio H. da S. Barbedo, Gustavo S. Araújo, João Maurício S. Moreira e
Ricardo S. Maia Clemente
Abr/2005
94
Simulação Histórica Filtrada: Incorporação da Volatilidade ao Modelo
Histórico de Cálculo de Risco para Ativos Não-Lineares
Claudio Henrique da Silveira Barbedo, Gustavo Silva Araújo e Eduardo
Facó Lemgruber
Abr/2005
95
Comment on Market Discipline and Monetary Policy by Carl Walsh
Maurício S. Bugarin and Fábia A. de Carvalho
Apr/2005
96
O que É Estratégia: uma Abordagem Multiparadigmática para a
Disciplina
Anthero de Moraes Meirelles
Ago/2005
97
Finance and the Business Cycle: a Kalman Filter Approach with Markov
Switching
Ryan A. Compton and Jose Ricardo da Costa e Silva
Aug/2005
98
Capital Flows Cycle: Stylized Facts and Empirical Evidences for
Emerging Market Economies
Helio Mori e Marcelo Kfoury Muinhos
Aug/2005
99
Adequação das Medidas de Valor em Risco na Formulação da Exigência
de Capital para Estratégias de Opções no Mercado Brasileiro
Gustavo Silva Araújo, Claudio Henrique da Silveira Barbedo,e Eduardo
Facó Lemgruber
Set/2005
100 Targets and Inflation Dynamics
Sergio A. L. Alves and Waldyr D. Areosa
Oct/2005
101 Comparing Equilibrium Real Interest Rates: Different Approaches to
Measure Brazilian Rates
Marcelo Kfoury Muinhos and Márcio I. Nakane
Mar/2006
102 Judicial Risk and Credit Market Performance: Micro Evidence from
Brazilian Payroll Loans
Ana Carla A. Costa and João M. P. de Mello
Apr/2006
103 The Effect of Adverse Supply Shocks on Monetary Policy and Output
Maria da Glória D. S. Araújo, Mirta Bugarin, Marcelo Kfoury Muinhos and
Jose Ricardo C. Silva
Apr/2006
28
104 Extração de Informação de Opções Cambiais no Brasil
Eui Jung Chang e Benjamin Miranda Tabak
Abr/2006
105 Representing Roomate’s Preferences with Symmetric Utilities
José Alvaro Rodrigues-Neto
Apr/2006
106 Testing Nonlinearities Between Brazilian Exchange Rates and Inflation
Volatilities
Cristiane R. Albuquerque and Marcelo Portugal
May/2006
107 Demand for Bank Services and Market Power in Brazilian Banking
Márcio I. Nakane, Leonardo S. Alencar and Fabio Kanczuk
Jun/2006
108 O Efeito da Consignação em Folha nas Taxas de Juros dos Empréstimos
Pessoais
Eduardo A. S. Rodrigues, Victorio Chu, Leonardo S. Alencar e Tony Takeda
Jun/2006
109 The Recent Brazilian Disinflation Process and Costs
Alexandre A. Tombini and Sergio A. Lago Alves
Jun/2006
110 Fatores de Risco e o Spread Bancário no Brasil
Fernando G. Bignotto e Eduardo Augusto de Souza Rodrigues
Jul/2006
111 Avaliação de Modelos de Exigência de Capital para Risco de Mercado do
Cupom Cambial
Alan Cosme Rodrigues da Silva, João Maurício de Souza Moreira e Myrian
Beatriz Eiras das Neves
Jul/2006
112 Interdependence and Contagion: an Analysis of Information
Transmission in Latin America's Stock Markets
Angelo Marsiglia Fasolo
Jul/2006
113 Investigação da Memória de Longo Prazo da Taxa de Câmbio no Brasil
Sergio Rubens Stancato de Souza, Benjamin Miranda Tabak e Daniel O.
Cajueiro
Ago/2006
114 The Inequality Channel of Monetary Transmission
Marta Areosa and Waldyr Areosa
Aug/2006
115 Myopic Loss Aversion and House-Money Effect Overseas: an
experimental approach
José L. B. Fernandes, Juan Ignacio Peña and Benjamin M. Tabak
Sep/2006
116 Out-Of-The-Money Monte Carlo Simulation Option Pricing: the join use
of Importance Sampling and Descriptive Sampling
Jaqueline Terra Moura Marins, Eduardo Saliby and Joséte Florencio do
Santos
Sep/2006
117 An Analysis of Off-Site Supervision of Banks’ Profitability, Risk and
Capital Adequacy: a portfolio simulation approach applied to brazilian
banks
Theodore M. Barnhill, Marcos R. Souto and Benjamin M. Tabak
Sep/2006
118 Contagion, Bankruptcy and Social Welfare Analysis in a Financial
Economy with Risk Regulation Constraint
Aloísio P. Araújo and José Valentim M. Vicente
Oct/2006
29
119 A Central de Risco de Crédito no Brasil: uma análise de utilidade de
informação
Ricardo Schechtman
Out/2006
120 Forecasting Interest Rates: an application for Brazil
Eduardo J. A. Lima, Felipe Luduvice and Benjamin M. Tabak
Oct/2006
121 The Role of Consumer’s Risk Aversion on Price Rigidity
Sergio A. Lago Alves and Mirta N. S. Bugarin
Nov/2006
30