Download Inflation Targeting in Emerging Market Economies

Inflation Targeting in
Emerging Market Economies
Arminio Fraga
Ilan Goldfajn
André Minella
Preliminary Version
April 2003
Comments are Welcome
1. Introduction
Motivation
 Inflation targeting (IT) is doing well in
general, although a bit less so in
emerging market economies (EMEs)
 Paper looks at EMEs and asks what
have we learned
 In a way, our experience in Brazil can
be seen as a stress test of IT!
Inflation Before and After the
Adoption of Inflation Targeting
Figure 1
Inflation Befor e and After Adoption of Inflation Tar geting (IT)
13.11
Emergin g Market Econom ies
14
12
10
8
5.95
%
Developed Eco nomies
6
3.72
2.50
4
2
0
Before IT
After IT (until 2002:2)
Before IT
After IT (until 2002:2)
Main issues





EMEs seem to face a lot of shocks, large
shocks
Some of them may be endogenous due to
weak institutions, historical problems, etc.
We discuss mainly how to manage monetary
policy when confronted with shocks. Other
policy recommendations are implicit or briefly
discussed
Key issue: credibility versus flexibility
We discuss the role of a communication
strategy, of bands, horizons, etc.
2. Stylized facts about inflation
targeting in EME


Higher volatility of inflation, GDP growth,
interest rate and exchange rate
Higher inflation level
Comparison of volatilities
Figure 2
Volatilities of Selected Variables of the period 1997-2002 (Average of Standard Deviation)
Interest Rate
6
5.47
5
GDP Growth
Inflatio n
4
3.40
3.37
3
2.07
2
Exchange Rate
(Standard Deviation/Average)
1.24
1.27
1
0.11
0.15
0
DE
EME
DE
EME
DE
EME
DE
EME
Trade-off Volatilities
F ig ure 3
Trade-o ff Volatilities Outpu t and Inflatio n (1997:1 - 2002:2)
7.0
6.5
Korea
Thailand
Standard Deviation of GDP Growth
6.0
5.5
5.0
4.5
4.0
New Zealand
3.5
Iceland
Chile
Peru
Norway
Sweden
2.0
Average D E
1.5
Colombia
Israel
3.0
2.5
Average EME
Mexico
Czech Republic
Poland
Brazil
Australia
Canada
1.0
Switzerland
South Africa
United Kingdom
0.5
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Standard Deviation of In flation
5.0
5.5
6.0
6 .5
7.0
7 .5
3. Model





Macro model for simulation
Small open economy
Combines features of Batini, Harrison, and
Millard’s (2001), and McCallum and Nelson’s
(2001) formulations
Derived from the intertemporal optimization
of households and firms
Price rigidity
4. Why is volatility higher?

Credibility building and disinflationary needs
Dominance issues: financial and fiscal

Larger shocks

4.1. Credibility building and
disinflationary needs


Both cases appear in the old macro
literature: adaptive expectations and
inertia (or persistence)
IT is an attempt to accelerate the
process of building credibility
Inflation before IT adoption
Figure 5
12-Month Inflation Right Before IT Adoption
45
Peru
40
35
30
Chile
%
25
Mexico
20
Israel
15
Poland
10
New Zealand
Czech Republic
Canada
South Korea
5
United Kingdom
Sweden
0
Dec-88
May-90
Sep-91
Australia
Jan-93
Jun-94
Developed Economies
Oct-95
Mar-97
Hungary
Colombia
Iceland
Brazil
South Africa
Norway
Switzerland Thailand
Jul-98
Emerging Market Economies
Dec-99
Apr-01
Sep-02
Inflation Target Averages
Figure 7
Inflation Target Averages - Central Points
20
18
16
14
%
12
10
8
6
4
2
0
1990
1991
1992
1993
1994
1995
Emerging Market Economies
1996
1997
1998
1999
Developed Economies
2000
2001
2002
Brazilian case: Central Bank’s
reaction function
Dependent Variable: Selic Interest Rate (Monthly Average)
Coefficients and standard errors
Regressors
Regression with
Inflation Report
Inflation
Expectations
Regression with
Market Inflation
Expectations
Constant
17.57***
(0.48)
16.68***
(0.69)
Interest Rate (t-1)
1.04***
(0.13)
1.36***
(0.18)
Interest Rate (t-2)
-0.20**
(0.08)
-0.56***
(0.15)
p
1.84
(1.19)
1.42*
(0.72)
-0.47***
(0.16)
-0.13
(0.17)
R-squared
0.9418
0.9539
Adjusted R-squared
0.9342
0.9465
LM Test for Autocorrelation of Residuals (p-values)
1 lag
0.5186
0.7408
4 lags
0.6766
0.5612
Deviation of Expected Inflation Rate from Target
Output Gap (t-1)
Notes: Standard error in parantheses. *, **, and *** indicate the coefficient is significant at 10%, 5%, and 1%
level, respectively. P refers to a p-value of 0.13. For the regression with Inflation Report inflation expectations the
sample is 1999:06-2002:06, and for that with market inflation expectations the sample is 2000:01-2002:06.
Brazilian case: Aggregate
supply curve
Table 5
Estimation of Aggregate Supply Curve for Brazil
Dependent Variable: Monthly Inflation Rate
Regressors
Constant
Coefficients and
standard errors
0.79**
(0.32)
Dummy*Constant
0.39***
(0.14)
Inflation Rate (t-1)
0.61***
(0.13)
Dummy*Inflation Rate(t-1)
-0.41**
(0.19)
Inflation Rate (t-2)
-0.09
(0.12)
Dummy*Inflation Rate (t-2)
-0.24
(0.19)
Unemployment (t-1)
-0.10**
(0.05)
Exchange Rate Change (t-1)
(Twelve-Month Average)
0.10***
(0.04)
Dummy for 2000:07
1.08***
(0.33)
R-squared
0.5537
Adjusted R-squared
0.5055
LM Test for Autocorrelation of Residuals (p-values)
1 lag
0.5454
4 lags
0.1081
Notes: Standard error in parantheses. *, **, and *** indicate the coefficient is significant at
10%, 5%, and 1% level, respectively. Sample: 1995:08 - 2002:06 (the sample starts 13 months
after the stabilization because we are using 12-month exchange rate change). Dummy refers to
the inflation-targeting period (1999:07-2002:06), unless otherwise noticed.
4.2. Dominance issues




General: central bank will/may inflate
Fiscal dominance
Financial dominance
External dominance (sub-investment
grade)
4.3. Shocks and “sudden stops”


Exchange rate volatiliy
Importance as shock factor
5. How to deal with higher
volatility

Answer: good communications and a
high degree of transparency
5.1. Target bands, horizons
and core






In a perfect world bands/horizons have no
role: central bank responds optimally given
exact size and nature of shock, parameters of
the economy, and preferences concerning
inflation
So, what, if any, is their role?
Bands: signalling/check point (focus on point
target)
Horizon: seems arbitrary
Core: no really good measure, confusing
Calendar year issues
5.2. Monetary policy committees,
meeting minutes, and inflation
reports



Existence of a monetary policy
committee (MPC)
Timely publication of detailed minutes
of MPC meetings
Quarterly Inflation Report
5.3. Shocks and adjusted targets:
the case of Brazil

1.
2.
3.
Methodology
Compute shocks (supply shocks) - include
future path
Accommodate direct impact, i.e., announce
an adjusted or intermediate target (path)
The chosen path will be a function of
parameters of the economy (e.g., inertia)
and inflation aversion
Brazil: Adjusted target 2003
Adjusted targets for 2003 and 2004
Itemization
2003
2004
(a)
Target for inflation set by CMN
4.0
3.75
(b)
Regulated-price shocks1
1.7
1.1
(c)
Inertia to be fought in following years2
2.8
0.6
4.2
1.0
of regulated prices
1.4
0.4
of market prices
2.8
0.6
8.5
5.5
Inherit inertia from the previous year (total)
(d)
Adjusted targets (=(a)+(b)+(c))
1) For the calculation of the shock, the effect of inertia and exchange rate on regulated-price inflation is deducted.
2) The inertia to be fought in the following years corresponds to 2/3 of the inertia inherited from the previous year.
5.4. IT and IMF programs


Net domestic assets (NDA) or
monetary aggregates targeting makes
little sense
Forward-looking quarterly targets with
consultation bands was the solution
we found
6. Conclusions



To deal with this more volatile environment,
we recommend:
High degree of transparency and a good
communications strategy
A methodology to calculate the convergence
path following a shock (adjusted targets)
Better IMF conditionality under inflation
targeting