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BANCO CENTRAL DE RESERVA DEL PERÚ
Preferences oftheCentralReserveBankofPeru
and optimalmonetarypolicyrulesintheinflation
targeting regime
Nilda Mercedes Cabrera Pasca*, Edilean Kleber da Silva
Bejarano Aragón** and Marcelo Savino Portugal***
* PUC-RJ, Brazil.
** UFPb, Brazil.
*** UFRGS, Brazil and CNPq
DT. N° 2011-010
Serie de Documentos de Trabajo
Working Paper series
Junio 2011
Los puntos de vista expresados en este documento de trabajo corresponden a los autores y no reflejan
necesariamente la posición del Banco Central de Reserva del Perú.
The views expressed in this paper are those ofthe authors and do not reflect necessarily the position of
the CentralReserveBankof Peru.
P
REFERENCES OFTHE
C
ENTRAL
R
ESERVE
B
ANK OF
P
ERU AND
O
PTIMAL
M
ONETARY
P
OLICY
R
ULES INTHE
I
NFLATION
T
ARGETING
R
EGIME
Nilda Mercedes Cabrera Pasca
1
Edilean Kleber da Silva Bejarano Aragón
2
Marcelo Savino Portugal
3
Abstract
This study aims to identify thepreferencesofthemonetary authority inthe Peruvian regimeofinflationtargeting
through the derivation ofoptimalmonetarypolicy rules. To achieve that, we used a calibration strategy based on
the choice of values ofthe parameters ofpreferences that minimize the square deviation between the true
interest rate and interest rate optimal simulation. The results showed that themonetary authority has applied a
system of flexible inflation targeting, prioritizing the stabilization of inflation, but without disregarding gradualism in
interest rates. On the other hand, concern over output stabilization has been minimal, revealing that the output
gap has been important because it contains information about future inflationand not because it is considered a
variable goal in itself. Finally, when the smoothing ofthe nominal exchange rate is considered inthe loss function
of themonetary authority, the rank order ofpreferences has been maintained andthe smoothing ofthe exchange
rate proved insignificant.
Keywords: Inflation target; CentralBank preferences, Optimalmonetarypolicy rules, CentralBankof Peru.
JEL Classification: C61, E52, E58.
1. Introduction
In recent years, a large number of academic researchers, as well as of researchers from other
areas, have strived to unravel the real incentives associated with policymakers’ actions in response to
macroeconomic development. Their justification is that monetarypolicy follows a systematic strategy,
driven by preferences related to the achievement of certain targets.
The empirical literature inthe past two decades has produced evidence in favor of improved
efficiency ofmonetarypolicyin countries which have adopted theinflationtargeting regime. Inthe case
of Peru, this regime was formally introduced in 2002 and, even though inflation targets had been
announced since 1994, there was no explicit institutional commitment towards their accomplishment.
Under the new regime, however, the Peruvian monetary authority lifted the control over themonetary
base as policy instrument and propounded an interest rate announcement policy. In this case, the
Central ReserveBankofPeru (CRBP) establishes its monetarypolicy instrument in order to meet the
targets outlined for the economic variables, such as inflation or output, in which the weights attached to
the loss function depend on thepreferences given to each ofthe established goals. On the other hand,
1
PhD student in Economics, PUC-RJ, Brazil. Email: nilda.pasca@econ.puc-rio.br
2
Assistant Professor of Economics, UFPb, Brazil. Email: eks_cme@yahoo.com.br
3
Professor of Economics, UFRGS, Brazil and CNPq researcher. Email: msp@ufrgs.br
2
notwithstanding an evident policy geared towards price stability in an inflationtargeting regime, the
monetary authority is less clear about its other monetarypolicy goals.
Given the objectives andthe instrument by which themonetary authority is guided inthe
inflation targeting regime, it is possible to rely on a functional relation (monetary rule) that combines both
elements and that also considers relevant economic variables. Therefore, ever since the seminal work
by Taylor (1993), several monetarypolicy rule specifications have been proposed to describe the
response ofcentral banks to economic variables. Conversely, in theory, the interest rate rules can be
derived as the solution to an intertemporal optimization problem restricted to the economic structure,
where themonetary authority seeks to minimize the loss associated with deviations ofthe objective
variables from their respective targets.
4
Nevertheless, as shown by Svensson (1999), the coefficients of
the interest rate rules derived through this method are complex combinations ofthe parameters
correlated with the economic structure and with themonetary authority’s preferences.
The present paper aims to identify thepreferencesofthe Peruvian monetary authority under the
inflation targetingregime by deriving optimalmonetarypolicy rules. Knowing about thepreferencesof
the authority in charge ofthemonetarypolicy is paramount, not only because this will allow
understanding the conduct ofthe interest rate policy, i.e., it will be possible to verify whether the
observed economic results are compatible with an optimalmonetary policy, but also because of its
influence on the formation of future expectations by economic agents. Due to the important role of
expectations in determining macroeconomic variables, the identification ofmonetary authority’s
preferences becomes even more important. Finally, this will also allow us to know what economic
variables enter the loss function.
In the present study, we will infer thepreferencesofthe CRBP by applying a calibration
strategy. Basically, this strategy is based on the selection of preference parameter values that minimize
the squared deviation between the actual interest rate path and a simulated optimal interest rate.
It is necessary to underscore, though, that the proposed method is different from those applied
to Peru. For instance, GMM, applied by Rodriguez (2008), is based on the estimation of a three-
equation system, namely: demand and aggregate supply and an equation for themonetary rule that
solves thecentral bank’s optimization problem, and whose results rely on the imposition of a finite policy
horizon (four quarters) for the problem with themonetary authority. In our work, it is not necessary to
impose a finite horizon, and just like Rodriguez (2008), we will use information on economic constraint
to solve the stochastic linear regulator problem. On the other hand, Bejarano (2001) estimates a VAR to
4
For further details, see Walsh (2010), Svensson (1999), and Castelnuevo and Surico (2003).
3
capture the dynamics ofthe economy, but he refers to a simple model for estimation ofthepreferences
of the Peruvian central bank.
Most ofthe international literature on policymakers’ preferences has been devoted to estimating
Federal Reserve (FED) preferences. Some noteworthy studies include the following: Salemi (1995), on
the use oftheoptimal linear quadratic control described by Chow (1981); Dennis (2004, 2006) and
Ozlale (2003), on maximum likelihood; Favero and Rovelli (2003), on GMM; llbas (2008), on Bayesian
methods; Söderlind et al. (2002) and Castelnuevo and Surico (2003), on a calibration process. These
studies demonstrated that the FED has given greater preference for inflation stabilization as well as for
interest rate smoothing, whereas output stabilization appears to have been neglected.
The international literature also addresses preference estimations for other central banks in
addition to the FED. For instance, Cecchetti and Ehrmann (1999) estimated preferences for 23
countries (including nine inflation targeters) and Cecchetti et al. (2002) estimated preferences for central
banks of countries belonging to the European Monetary Union. In both studies, the authors used VAR
and found evidence that the trade-off between inflationand output has varied considerably among
different countries, with heavier weight being placed on inflation rather than on output variability. Collins
and Siklos (2004) estimated thepreferences for central banks of Canada, Australia, New Zealand and
the United States (USA), using GMM, and found that central banks can be described by an optimal
inflation targetingregime with significant weight on interest rate smoothing and a lesser weight on the
output gap. Tachibana (2003) estimated thepreferences for central banks of Japan, the UK andthe
U.S. after the first oil shock. The author showed that these countries increased their aversion to inflation
volatility, especially from the 1980s onwards. Rodriguez (2006) estimated thepreferences for theBank
of Canada for different subsamples and, to that purpose, he used GMM. The author evidenced that the
monetary authority’s preferences changed across regimes, chiefly the parameter associated with the
implicit inflation target, which has significantly decreased. Finally, Silva and Portugal (2009) identified
the preferencesoftheCentralBankof Brazil (CBB) intheinflationtargetingregime using a calibration
process and found evidence that the CBB adopted a flexible inflationtargeting regime, placing larger
emphasis on inflation stabilization. Moreover, the authors showed that the CBB was much more
concerned with the smoothing ofthe Selic interest rate than with output stabilization.
Empirical studies on thepreferencesofthe CRBP are scarce. Within this line of research, we
highlight three studies: Goñi and Ormeño (1999), using GMM andmonetary base as monetarypolicy
instrument, determined thepreferencesof CRBP for the 1990s. The authors found that the CRBP had a
greater preference for inflation stabilization and for exchange rate depreciation and a lesser preference
for the output gap. Inthe same vein as Cecchetti and Krause (2001) and Cecchetti and Ehrmann
4
(1999), Bejarano (2001) estimated thepreferencesofthe CRBP for the 1990s. The author
demonstrated that the CRBP had a larger preference for inflation rather than for output variability,
concluding that the behavior ofmonetarypolicyinthe 1990s was not far from inflation targeting. Finally,
Rodriguez (2008), following Favero and Rovelli (2003), estimates thepreferencesofthe CRBP for
different regimes.
5
Using GMM, the author found evidence that the implicit inflation target has
significantly decreased and that the Peruvian monetarypolicy may have been efficiently conducted in
the last regime (1994:2-2005:4).
The present paper contributes to the existing empirical literature on Peru using a different
sample, specifically theinflationtargeting regime, and also a different method (calibration) to determine
the preferencesofthe CRBP. Results showed that the Peruvian monetary authority intheinflation
targeting regime has adopted a flexible monetary policy, being largely concerned with inflation stability,
and followed by considerable concern with interest rate smoothing. However, the preference for output
stability and exchange rate smoothing has been negligible.
Our study is organized into three sections, in addition to the introduction. Section 2 shows the
development ofthe theoretical model andthecentral bank’s optimization problem, as well as the
strategy for calibration ofthemonetary authority’s preferences. Section 3 addresses the estimation
results for the structure ofthe economy and identifies thepreferencesofthe Peruvian monetary
authority, based on a monetarypolicy analysis. Section 4 concludes.
2
.
The Macroeconomic Model
The CRBP has a dynamic optimal control problem whose solution is contemplated in its policy
actions. These are theoptimal responses ofthemonetary authority to economic development, which
are captured by the relationships between state variables andthe control variable (the monetarypolicy
instrument).
In what follows, we describe the dynamics ofthe state variables based on the structure ofthe
economy that restricts the policymaker’s optimization problem as well as the derivation oftheoptimal
monetary policy rule. Finally, we show the steps used inthe calibration strategy for determining the
policy preferencesofthe CRBP.
5
For further details on the classification ofmonetarypolicy regimes in Peru, see Castillo et al. (2007).
5
2.1 Economic Structure
When central banks optimize, they are subject to the restriction imposed by the behavior ofthe
economic structure. In this paper, we describe a structural macroeconomic model for the Peruvian
economy with backward-looking expectations. The proposed model is based on Rudebusch and
Svensson (1998, 1999) and Silva and Portugal (2009). The dynamics governing the four equations that
make up the model is given by:
(
)
1 1 2 1 3 2 4 1 5 1 , 1
t t t t t t t t
q q y
π
π α π α π α π α α ξ
+ − − − − +
= + + + − + +
(1)
1 1 2 1 3 1 4 , 1
t t t t t y t
y y y r tt
β β β β ξ
+ − − +
= + + + +
(2)
1 , 1
t t q t
q q
ξ
+ +
= +
(3)
1 1 , 1
t t tt t
tt tt
γ ξ
+ +
= +
(4)
where:
t
π
is the annualized quarterly inflation rate, measured by
(
)
1
400* log( ) log( )
t t
p p
−
−
, where
t
p
is the consumer price index for the metropolitan region of Lima;
t
q
is the nominal exchange rate;
t
y
is the output gap percentage between the real GDP and potential GDP, i.e.,
(
)
*
100 * log( ) log( )
t
t t
y GDP GDP
= −
, where
t
GDP
and
*
t
GDP
are the real and potential gross
domestic product, respectively;
t
tt
is the terms of trade gap defined as the percentage difference ofthe
terms of trade from their trend, i.e.,
(
)
*
,
100 * log( ) log( )
t real t t
tt tt tt
= −
, where
real
tt
denotes the
terms of trade index and
*
t
tt
is the potential terms of trade index. For the gap variables, the trend values
were calculated using the Hodrick-Prescott filter. Finally,
t
r
stands for the real interest rate, defined as
the difference between the nominal interest rate and regarded as monetarypolicy instrument,
t
i
, and
inflation rate,
t
π
. All variables are expressed as deviations from the mean; therefore, no constant
appears in system (1) - (4).
The terms
, 1
t
π
ξ
+
,
, 1
y t
ξ
+
,
, 1
q t
ξ
+
and
, 1
tt t
ξ
+
are construed as supply shocks, demand shocks,
exchange rate shocks and terms of trade shocks, respectively.
Equation (1) can be seen as an aggregate supply that shows that the current inflation rate
depends on its lagged values, on the fluctuation ofthe exchange rate inthe previous period and on the
two-period lag ofthe output gap. The verticality ofthe aggregate supply is imposed by the restriction
that the sum ofthe lagged inflation parameters andofthe fluctuation inthe exchange rate should be
equal to 1. This means that any exchange rate depreciation is totally transferred to prices inthe long
run.
6
The aggregate demand, expressed by equation (2), shows the relationship ofthe output gap
with its lagged values, with the real interest rate lagged two periods and with the terms of trade gap
lagged one period.
6
The importance to include the latter variable inthe aggregate demand equation is
due to that Peru, for be a small open economy, it is vulnerable to external shocks that affect the
aggregate demand. The terms of trade, which have a close relationship with economic fluctuations,
mainly after the implementation oftheinflationtargetingregime (Castillo et al., 2007),
7
are one ofthe
variables that capture this vulnerability.
According to equations (3) and (4), the exchange rate is assumed to follow a random walk and
the terms of trade are believed to follow a first-order autoregressive process for the sake of
simplification ofthe model.
8
The coefficients that follow the exchange rate depreciation andthe output gap inthe aggregate
supply equation are expected to be positive, i.e.
4 5
0 0
and
α α
> >
, respectively. In addition, a
negative sign is expected for the real interest rate coefficient inthe aggregate demand equation,
3
0
β
<
, and so is a positive sign for the terms of trade coefficient,
1
0
γ
>
.
Although the model described here is parsimonious, it has two advantages: i) it simplifies the
solution to the intertemporal optimization problem by the policymaker, as it simplifies that state-space
representation ofthe economic structure; and ii) it captures an important channel for the transmission of
monetary policy, the aggregate demand channel. In regard to the latter, an increase inthe interest rate,
t
i
, which causes the real interest rate to deviate from its long-term trend, reduces the output gap after
two quarters andtheinflation rate after four quarters.
While the empirical success ofthe proposed model has been documented by studies conducted
for developed economies, such as the works of Rudebusch and Svensson (1998, 1999) for the U.S.,
and for emerging economies, undertaken by Silva and Portugal (2009) for Brazil, it is important to
pinpoint the advantages and disadvantages of using this type of backward-looking models.
Backward-looking models have been supported by both academic economists andmonetary
authorities, and their application in several research studies is frequent, as occurs in Rudebusch and
Svensson (1998, 1999), Favero and Rovelli (2003), Ozlale (2003), Dennis (2006), Collins and Skilos
(2004), among others. In addition, Fuhrer (1997) compared backward-looking and forward-looking
models, with favorable results for the former. According to Estrella and Fuhrer (2002), models with
6
The assumption that the output gap depends on the real interest rate lagged two periods is supported by the analysis of
cross-correlograms and by the evidence provided by Castillo et al. (2007, p.35).
7
The importance of terms of trade to the Peruvian aggregate demand is highlighted by Castillo et al. (2007) and by the
Modelo de Proyección Trimestral del BCRP (2009).
8
The assumption that the exchange rate equation follows a random walk is based on the best fit for these data, as described
in Section 3.
7
forward-looking expectations tend not to fit the data well, unlike the models proposed by Rudebusch and
Svensson (1998, 1999). Woodford (2000, 2004), however, ascribes the fact that monetarypolicy is
optimal, to some extent, to its history, or in other words, to its backward-looking behavior. Finally,
models that employ rational expectations have been often unable to do without backward-looking
elements in models for the structure ofthe economy (Collins and Skilos, 2004).
On the other hand, backward-looking models show considerable parameter instability, and are
subject to the Lucas critique (Lucas, 1976). To overcome this hindrance, inthe present paper, we
consider one single monetaryregime such as the “inflation targeting regime.”
2.2 CentralBankPreferencesandOptimalMonetaryPolicy
The monetary authority’s goal is to minimize the expected value from the loss function:
0
t t
E LOSS
τ
τ
τ
δ
∞
+
=
∑
(5)
where:
(
)
( )
2
2
* 2
1
i
a
t t y t t t
LOSS y i i
π
λ π π λ λ
∆ −
= − + + − (6)
where
δ
is the intertemporal discount rate,
0 1
δ
< <
,
t
E
is the expectations operator conditional on
the set of information available at t
andin which all weights are greater than or equal to
zero,
0, 0 0
i
y
and
π
λ λ λ
∆
≥ ≥ ≥
.
9
With this objective function, themonetary authority is assumed to
stabilize annual inflation,
3
0
1
4
a
t t j
j
π π
−
=
=
∑
, around an inflation target,
*
π
, to maintain the output gap
closed at zero and to smooth the nominal interest rate.
We take for granted that theinflation target is fixed over time and normalized to zero given that
all variables are expressed as deviations from their respective means.
10
Output gap targets and interest
rate smoothing are also assumed to be zero. The parameters that measure themonetary authority’s
policy preferences,
,
i
y
and
π
λ λ λ
∆
, indicate the importance given by themonetary authority to
stabilization ofinflationandofthe output gap, and to interest rate smoothing, respectively. Finally, we
assume that policypreferences add up to one, i.e.,
1
i
y
π
λ λ λ
∆
+ + =
.
9
When discount factor
0
δ
→
, intertemporal loss function (6) approaches the unconditional mean ofthe loss function at
time t:
[
]
[
]
[
]
*
1
i
a
t t y t t t
E LOSS Var Var y Var i i
π
λ π π λ λ
∆ −
= − + + −
(see Rudebusch and Svensson, 1999).
10
Expressing all the variables that restrict the structure ofthe economy to deviation ofthe mean from theinflation target
normalized at zero does not alter the derivation ofmonetary authority’s preferences, as demonstrated by Dennis (2006),
Castelnuevo and Surico (2003) and Ozlale (2003).
8
The formulation ofthe loss function in (6) has been commonly used inthe literature to identify
central bank preferences, and is attractive for numerous reasons. First, a quadratic loss function subject
to a linear restriction facilitates the derivation ofoptimalmonetarypolicyrules by means of restricted
optimization methods, specifically with respect to the stochastic linear regulator problem.
11
Second, the
specification of loss function (6) allows themonetary authority to smooth the nominal interest rate, in
addition to the goals of stabilization ofinflationand output. Finally, as shown by Woodford (2002), a
specification of loss function similar to (6) can be derived as a second-order approximation of an
intertemporal utility function ofthe representative agent.
Many are the reasons for including interest rate smoothing inthecentral bank’s loss function.
Amongst the most common reasons, we highlight the following: uncertainty over the key economic
parameters caused by uncertainty over economic information that, consequently, encourages the
central bank to adopt prudent monetarypolicy actions in an attempt to reduce uncertainty costs
(Castelnuovo and Surico, 2003, Sack and Wieland, 1999); difficulty in understanding whether the
problems under analysis originate from merely economic shocks or from measurement errors inthe
data; large interest rate oscillations may lead to loss of reputation or of credibility ofthemonetary
authority (Dennis, 2006); large interest rate volatility may result in capital loss, thus impairing the
financial sector (Ozlale, 2003); announcement of a short disinflation horizon might not measure up to
the expectations ofthe economic agents and, therefore, it might not be dependable, requiring some
gradualism (Rojas, 2002). Finally, the inclusion of interest rate smoothing together with other relevant
variables (such as inflation, output and exchange rate) for a small open economy is crucial in an inflation
target regimein order to try to meet theinflation target.
Inthe current inflationtargeting regime, the Peruvian monetary authority has apparently paid a
lot of attention to the evolutionary behavior ofthe exchange rate. Inthe present study, this possibility is
contemplated for the following reasons. First, unlike other emerging economies which have adopted the
inflation targeting regime, the Peruvian currency is partially dollar-pegged, where the exchange rate is
the most relevant financial asset price for the stability ofthe financial system. Thus, in dollarized
economies such as Peru, abrupt exchange rate fluctuations result in high costs for the financial system,
as well as for families whose debts are denominated in U.S. dollars (Inflation Reports CRBP, 2009).
Second, monetary authority’s interventions inthe exchange rate market are believed to have a
disguised precautionary motive – accumulation of international reserves to tackle negative external
shocks.
12
Given these aspects, a second exercise was developed, where exchange rate smoothing,
11
For further details, see Miranda and Fackler (2002, p. 233) and Ljungqvist and Sargent (2004, p.110-114)
12
For further details on CRBP interventions inthe exchange rate market, see Inflation Reports (2008, 2009).
9
q
∆
, is regarded as the fourth goal ofthe Peruvian monetary authority. In this case, the loss function is
described as:
(
)
( ) ( )
2
2 2
* 2
1 1
i
a
t t y t t t q t t
LOSS y i i q q
π
λ π π λ λ λ
∆ − ∆ −
= − + + − + −
(7)
Where the sum ofthe coefficients is assumed to be one, i.e.,
1
i q
y
π
λ λ λ λ
∆ ∆
+ + + =
.
To derive theoptimalmonetarypolicy rule, first we have to set the optimization restriction in
state-space form. The restriction on the optimization problem is described by the structure ofthe
economy, given by system (1)-(4). This system has a convenient state-space representation, given by:
1 1
t t t t
X AX Bi
ξ
+ +
= + +
(8)
Where the elements of equation (8) are given by:
[
]
'
'
1 2 3 1 1 1
t t t t t t t t t t t
X y y q q tt i
π π π π
− − − − − −
=
(9)
1 2 3 5 4 4
3 1 2 4 3
1
1
0 0 0 0 0
1 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0
; ;
0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1
t
A B
α α α α α α ξ
β β β β β
ξ
γ
+
−
−
= = =
, 1
, 1
, 1
, 1
0
0
0
0
0
0
t
y t
q t
tt t
π
ξ
ξ
ξ
+
+
+
+
(10)
where
1
t
X
+
is a 10x1 vector, which represents the state variables,
t
i
is the control variable for the
policy (nominal interest rate) and
1
t
ξ
+
is a vector containing supply and demand shocks, which are
assumed to be normally i.i.d with zero mean and constant variances.
After that, thecentral bank’s loss function must be set in its matrix form. To do that, it is
necessary to express it in terms of state and control variables, as follows:
t x t i t
Z C X C i
= +
(11)
where:
13
13
Vector Z, if the exchange rate is regarded as objective variable, is written as:
'
'
1 1
a
t t t t t t
Z y i i q q
π
− −
= − −
,
where the procedure for derivation oftheoptimalmonetary rule is the same in both cases.
[...]... identifying the CRBP preferences To accomplish that, we chose the weights that determine themonetary authority’s preferences for inflationand output stabilization and interest rate smoothing inthe loss function ofthecentralbank that minimizes the squared deviation between the actual interest rate andtheoptimal interest rate Theoptimal interest rate is derived based on the true history ofthe economy... efficiency ofmonetarypolicyin several countries, specifically in those that have adopted theinflationtargetingregimePeru has formally used theinflationtargetingregime since 2002, a decision that was made by themonetary authority after a significant reduction inthe growth of price level inthe 1990s Therefore, monetary base has been put aside as a monetarypolicy instrument, and an interest... due to the methodology used did not allow the incorporation of forward-looking components inthe structure ofthe economy, such as the incorporation of inflationary expectations inthe supply equation On the other hand, another important factor not considered in this work is the incorporation of a variable that captures the problem of financial dollarization in Peru, which may influence the Peruvian... by the CRBP intheinflationtargetingregimeThe present sampling period ends in 2008:02,19 as the macroeconomic variables were influenced by the effects ofthe world financial crisis from the second half of 2008 onwards,20 especially by the reduction inthe terms of trade caused by a slump inthe price of metals.21 17 For the case in which interest rate smoothing λ∆q is considered, this smoothing... is an interesting result This may not have been an ultimate concern ofthemonetary authority intheinflationtargetingregime ( λy = 0.001) Despite the low weight of output gap on thecentralbank s loss function, its insertion into the model is important as this variable is key to generating information on the behavior of future inflation (Dennis, 2006) On the other hand, a weight of 0.30 on interest... interest rate smoothing shows the importance that the Peruvian monetary authority has attached to the gradualist approach to the interest rate intheinflationtargetingregime as response to inflation flexibility, mainly from 2002 onwards, when interest rate movements were directed toward stabilizing inflationand maintaining preventive actions in order to sustain economic agents’ inflation expectations... ofpreferences that minimize the squared deviation between the true interest rate path andthe simulated interest rate The results showed that the Peruvian monetary authority during theinflationtargetingregime can be effectively described by a flexible inflationtargeting policy, giving priority to inflation stabilization without overlooking the interest rate movements which, since 2002, have been... observed interest rate path Figure 2 shows the path for theoptimal interest rate associated with thepreferences obtained by the calibration strategy andthe true interest rate path approximated by the interbank rate.32 Note that theoptimal interest rate captures the main movements ofthe observed interest rate However, there are some inconsistencies, especially inthe first periods For instance, the monetary. .. 2009, CentralBankPreferencesandMonetaryRules under theInflationTargetingRegimein Brazil” Brazilian Review of Econometrics, vol 29, n.1 SÖDERLIND, P.; SÖDERSTRÖM, U.; VREDIN, A, 2002, “Can calibrated New-keynesian models ofmonetarypolicy fit the facts?” Stockholm: Sveriges Riksbank, Working paper, n 140 33 SVENSSON, L E O, 1999, InflationTargeting as a MonetaryPolicy Rule” Journal of Monetary. .. coefficients ofthe output gap and of the terms of trade lagged one period were statistically significant (see Table 2) On the other hand, the coefficient of the real interest rate was not statistically significant Even though this result suggests a minor initial role ofmonetary policy, the impact of the lagged values of the output gap on the aggregate demand is remarkable, implying that the response ofthe . under the
inflation targeting regime by deriving optimal monetary policy rules. Knowing about the preferences of
the authority in charge of the monetary policy. identify the preferences of the monetary authority in the Peruvian regime of inflation targeting
through the derivation of optimal monetary policy rules.