Macro II, Tópico 1:
Política Monetária e Fiscal: Inflation
Targeting em Mercados Emergentes
(14 de agosto de 2007, 16h)
Prof. Márcio Garcia
Política Monetária e Fiscal: Inflation
Targeting em Mercados Emergentes
Leituras obrigatórias:
 Fraga, A. et al, “Inflation Targeting in Emerging
Market Economies”, in NBER Macroeconomics
Annual 2003.
 Mishkin, F., “Can Inflation Targeting Work in
Emerging Market Countries?”, NBER WP # 10646.
 FMI, World Economic Outlook, setembro de 2005.
Capítulo IV. Disponível em www.imf.org.
FMI, “Inflation Targeting and the IMF”, 16/03/2006.
Disponível em www.imf.org.
What is inflation targeting?

Inflation targeting (IT) has no unanimous definition. Mishkin
[2004] formally defines IT as comprising five components:
 the public announcement of medium-term numerical
targets for inflation;
 an institutional commitment to price stability as the
primary goal of monetary policy, to which other goals are
subordinated;
 an
information inclusive strategy in which many
variables, and not just monetary aggregates or the
exchange rate, are used for deciding the setting of policy
instruments;
 increased transparency of the monetary policy strategy
through communication with the public and the markets
about the plans, objectives, and decisions of the
monetary authorities; and
 increased accountability of the central bank for attaining
its inflation objectives.
What is different about IT?

According to the World Economic Outlook report (IMF
[2005]), the key distinctions between IT other regimes
are the following two.


The central bank is mandated, and commits to, a unique
numerical target in the form of a level or a range for annual
inflation. A single target for inflation emphasizes the fact that
price stabilization is the primary focus of the strategy and the
numeric specification provides a guide to what the authorities
intend as price stability.
The inflation forecast over some horizon is the de facto
intermediate target of policy. For this reason inflation targeting is
sometimes referred to as “inflation forecast targeting”
(Svensson, 1998). Since inflation is partially predetermined in
the short term because of existing price and wage contracts
and/or indexation to past inflation, monetary policy can only
influence expected future inflation. By altering monetary
conditions in response to new information, central banks
influence expected inflation and bring it in line over time with the
inflation target, which eventually leads actual inflation to the
target.
IT and other Monetary Policy Strategies

A synthesis of these two definitions of IT may be found in John
Taylor´s remark that the main difference between IT and other
monetary policy regimes, e.g. money or exchange rate
targeting, was that IT used all the information contained in the
macro variables to set the basic interest rate, while other
regimes used only part of the information available to determine
the interest rate. In a seminal paper (Taylor [1999]), where he
analyzed the monetary policy rules implied by the different US
monetary policy regimes since 1880, he showed the properties
that good monetary policy rules in the US must have, e.g., to
respond … to inflation and real output more aggressively than
during the 1960s and 1970s or than during the international
gold standard—and more like the late 1980s and 1990s. That
seems to be the key to successful monetary policy strategies,
use of all information to appropriately set interest rates to guide
inflation expectations. IT is a way to achieve this.
What are the alternatives to IT?

Monetary targeting:



Instability of money demand;
Money multiplier and money velocity vary a lot.
Good for countries where the CB has little
credibility and analytical capabilities (money
targeting is very easy to implement and money
data are readily available).
What are the alternatives to IT?

Exchange rate targeting:

Two types:



Fixed exchange rates (currency board, monetary union, and
unilateral dollarization);
Fixed-but-adjustable-exchange
rates
(crawling
pegs,
crawling bands, etc.)
Drawbacks:



Monetary policy is “imported” from a foreign country whose
business cycle may differ;
Possibility of speculative attacks;
Domestic prices bear all the burden of real exchange rate
adjustment.
Why is IT more and more popular?

Because apart from it (or IT) there is only the
Nike™ approach…
How widely used is IT?

In 2005, there were 21 countries that adopted
IT as their monetary policy strategy: eight
industrial countries and 13 emerging markets
(EMs) (IMF [2005]). Table 4.1 of IMF [2005]
lists the inflation targeters, as well as other
relevant information on how IT is
implemented in those countries.
Table 4.1 INFLATION TARGETERS – source: World Economic
Outlook, ch4, IMF [2005]
Inflation Targeting
Adoption Date
Unique Numeric
Target = Inflation
Inflation Rate at
Start
Current Inflation
Target (percent)
Forecast
Process
Publish
Forecast
Emerging Market Countries
Israel
Czech Republic
Korea
Poland
Brazil
Chile
Colombia
South Africa
Thailand
Mexico
Hungary
Peru
Philippines
Slovak Rep.
Indonesia
Romania
1997 : Q2
1998 : Q1
1998 :Q2
1999 : Q1
1999 : Q2
1999 : Q3
1999 : Q3
2000 : Q1
2000 : Q2
2001 : Q1
2001 : Q3
2002 : Q1
2002 : Q1
2005:Q1
2005:Q3
2005:Q3
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
8.5
13.1
3.2
9.9
3.3
2.9
9.3
2.3
1.7
8.1
10.5
-0.8
3.8
3.2
7.8
8.8
1-3
3 (+/- 1)
2.5 - 3.5
2.5 (+/- 1)
4.5 (+/- 2)
2-3
3.5 - 4.5
3-6
1.5 - 2.5
3
3 (+/- 1)
2 (+/- 1)
4-5
3,5(+/-1)
5,5 (+/-1)
7,5(+/- 1)
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Industrial Countries
New Zealand
Canada
United Kingdom
Australia
Sweden
Iceland
Norway
1990 : Q1
1991 : Q1
1992 : Q4
1993 : Q1
1993 : Q1
2001 : Q1
2001: Q1
Y
Y
Y
Y
Y
Y
Y
7.0
6.2
3.6
1.9
4.8
3.9
3.7
1 -3
1-3
2
2-3
2 (+/- 1)
2.5
2.5
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Source: National Authorities
Why is IT more difficult in EM?

(Mishkin [2004], Fraga, Golfajn and Minella [2003]).
 EMs generally have weak fiscal institutions, which leads to fiscal
dominance, i.e., the lack of the ability to freely raise the interest
rate because of the negative fiscal impact.
 EMs generally have weak financial institutions, which leads to
financial dominance, i.e., the lack of the ability to freely raise the
interest rate because of the fear of general bankruptcy of financial
institutions. This also includes poor prudential regulation and
supervision.
 EMs’ monetary institutions lack credibility, which may require too
high an interest rate to achieve the inflation target, with negative
impacts on output growth.
 Many
EMs suffer from currency substitution and liability
dollarization, which may seriously hamper the ability to let the
exchange rate float. Fear of floating (Calvo and Reinhart [2002])
may arise.
 EMs are very vulnerable to the reversal of capital flows. Large
external shocks cause large damages to the EMs, a phenomenon
know as sudden stop (Calvo and Reinhart [2000], Calvo, Izquierdo
and Mejia [2004]). This is termed by Fraga, Goldfajn and Minella
[2003] external dominance.
Is IT suitable for EM?


Even though some of all the factors above
may be true for a given EM, the appraisal of
the experience of the EMs that have opted for
IT seem to run favorably to IT.
Let’s see the empirical evidence…
IT performance in EM
IT performance in EM
IT performance in EM
IT performance in EM
IT performance in EM
IT performance in EM
IT performance in EM
Is IT suitable for EM?

Although the time since the adoption of IT by EMs is
short, the IMF report was able to draw a few
conclusions regarding the comparative performance
of IT and non-IT EMs. … Inflation targeting appears
to have been associated with lower inflation, lower
inflation expectations, and lower inflation volatility
relative to countries that have not adopted it. There
have been no visible adverse effects on output, and
performance along other dimensions—such as the
volatility of interest rates, exchange rates, and
international reserves—has also been favorable
(IMF [2005]).
A performance de IT no Brasil
YEAR
1999
2000
2001
2002
2003
2003*
2003*
2004
2004*
2005
2005**
2006
2007
2008
2009
SETTING DATE
30/6/1999
30/6/1999
30/6/1999
28/6/2000
28/6/2001
27/6/2002
21/1/2003
37/6/2002
25/6/2003
25/6/2003
23/9/2003
30/6/2004
22/6/2005
22/6/2006
26/6/2007
TARGET
8,00% ±2,00%
6,00% ±2,00%
4,00% ±2,00%
3,50% ±2,00%
3,25% ±2,00%
4,00% ±2,50%
8,50% ±2,50%
3,75% ±2,50%
5,50% ±2,50%
4,50% ±2,50%
5,10%
4,50% ±2,00%
4,50% ±2,00%
4,50% ±2,00%
4,50% ±2,00%
CPI INFLATION
8,94%
5,97%
7,67%
12,56%
9,30%
7,60%
5,69%
3,14%
3,6%***
3,89%***
3,98%***
* Revised Targets
** Objective
*** Consensus Forecasts (means) on June 22, 2007
Source: Brazilian Central Bank Web Page (www.bcb.gov.br)
GDP GROWTH
0,25%
4,31%
1,31%
2,66%
1,15%
5,71%
2,94%
3,70%
4,3%***
4,18%***
4,15%***
O modelo básico de IT do BCB

O ponto de partida do modelo macroeconômico
utilizado pelo BCB para a condução da política
monetária no regime de metas de inflação,
doravante denominado modelo estrutural, é o
trabalho de Bogdanski, Tombini e Werlang (2000)[1].
Trata-se de um modelo com quatro variáveis
básicas: a taxa de juros, a taxa de inflação, o hiato
do produto e a taxa de câmbio.
Bogdanski, J., A. Tombini e S. Werlang (2000) “Implementing
Inflation Targeting in Brazil”, BCB Working Paper Series nº 1.
[1]
O modelo básico de IT do BCB:
A curva IS

O lado da demanda agregada é descrito por uma
Curva IS, que relaciona o hiato do produto à taxa de
juros real (medida pela taxa do swap DI-pré de 180
dias negociado na BM&F), a uma medida do grau de
confiança do consumidor e a valores defasados do
hiato. Para medir o hiato do produto é necessário
estimar o produto potencial da economia, variável
esta que é não observável.
ht  0  1dt  2ht 1  3st 1  4 ln(ICt 1 )  
O modelo básico de IT do BCB:
A curva de Phillips

O lado da oferta é descrito por uma Curva de Phillips que
relaciona a variação dos preços livres (excluindo os itens
“Aluguéis” e “Cursos” do IPCA) a suas variações passadas, ao
hiato do produto e à variação do custo em reais dos bens
importados. Esta última componente incorpora os efeitos da
variação da taxa de câmbio R$/US$ sobre a inflação (passthrough). Os preços livres são aqueles determinados
livremente pelo mercado, e contrastam com os chamados
preços administrados por contrato ou monitorados, cuja
determinação reflete algum tipo de participação do Governo.
~t  1~t 2  2~t 3  3 (et  et 1 )  4 (et 1  et 2 )  5ht 2  t
O modelo básico de IT do BCB:
A taxa de câmbio

De acordo com o BCB, a taxa de câmbio é
determinada por uma equação de paridade
descoberta da taxa de juros (UIP), ou seja,
ela reflete as variações ocorridas nas taxas
de juros doméstica e internacional, no prêmio
de risco e choques nas expectativas em
relação ao seu comportamento futuro. O
ajuste econométrico da UIP aos dados é
notoriamente ruim.
O modelo básico de IT do BCB:
A função de reação do BC

Como o BC é o formulador da função de
reação, ele não divulga uma. O que faz é
divulgar as distribuições de probabilidades
para inflação e crescimento de determinadas
trajetórias das taxas de juros.
O modelo básico de IT do BCB:
Previsões de Inflação
O modelo básico de IT do BCB:
Previsões de Inflação
Fonte: Banco Central do Brasil
O modelo básico de IT do BCB:
Previsões de Inflação
Fonte: Banco Central do Brasil
O modelo básico de IT do BCB:
Previsões de Inflação
Fonte: Banco Central do Brasil
O modelo básico de IT do BCB:
Previsões de Inflação
Fonte: Banco Central do Brasil
O modelo básico de IT do BCB:
Previsão de Crescimento do PIB
Fonte: Banco Central do Brasil
O modelo básico de IT do BCB:
Aderência
Fonte: Banco Central do Brasil
Nominal and Real Exchange Rates and CPI Inflation
20%
4,3
18%
4
3,7
16%
3,4
14%
3,1
real / dólar
12%
2,8
10%
2,5
8%
2,2
6%
1,9
4%
1,6
Cpi Inflation (Last 12 Months- LHS)
Nominal Exchange Rate (BRL/USD) - RHS
Real Exchange Rate - RHS
jan/07
jul/06
jan/06
jul/05
jan/05
jul/04
jan/04
jul/03
jan/03
jul/02
jan/02
jul/01
jan/01
jul/00
1
jan/00
0%
jul/99
1,3
jan/99
2%
Real and Nominal Interest Rate and CPI Inflation
90%
45%
40%
75%
35%
60%
30%
25%
45%
20%
30%
15%
10%
15%
5%
0%
0%
-5%
-15%
-10%
jan/07
set/06
mai/06
jan/06
set/05
mai/05
jan/05
set/04
mai/04
jan/04
set/03
mai/03
jan/03
set/02
mai/02
jan/02
set/01
mai/01
jan/01
set/00
mai/00
jan/00
set/99
mai/99
-15%
jan/99
-30%
CPI Inflation
Nominal Interest Rate (SELIC)
Real Interest Rate (annualized)
Real Interest Rate (with last 12 month inflation - annualized rate)
EMBI+ BRAZIL SPREAD
EMBI+ SPREAD
01/jul/07
01/jan/07
01/jul/06
01/jan/06
01/jul/05
01/jan/05
01/jul/04
01/jan/04
01/jul/03
01/jan/03
01/jul/02
01/jan/02
01/jul/01
01/jan/01
01/jul/00
01/jan/00
01/jul/99
01/jan/99
01/jul/98
01/jan/98
01/jul/97
01/jan/97
01/jul/96
01/jan/96
01/jul/95
01/jan/95
01/jul/94
01/jan/94
01/jul/93
01/jan/93
01/jul/92
31/dez/91
Country - Risk: EMBI+ e EMBI+ BRAZIL
3000
2500
2000
1500
1000
500
0
-1%
NET Public Debt (% of GDP)
-2%
Primary Fiscal Balance (Last 12 Months - % os GDP)
abr/07
dez/06
ago/06
abr/06
dez/05
ago/05
abr/05
dez/04
ago/04
abr/04
dez/03
ago/03
abr/03
dez/02
ago/02
abr/02
dez/01
ago/01
abr/01
dez/00
ago/00
abr/00
dez/99
ago/99
abr/99
dez/98
ago/98
abr/98
dez/97
ago/97
abr/97
dez/96
Balanço Fiscal Primário (% do PIB)
6%
65
5%
60
4%
55
3%
50
2%
45
1%
40
0%
35
30
25
Dívida Pública Líquida (% do PIB)
Primary Fiscal Balance (% of GDP)
Inflation Expectations, target and Interest Rate
29,00%
12,70%
11,70%
27,00%
10,70%
25,00%
9,70%
8,70%
23,00%
7,70%
6,70%
21,00%
5,70%
19,00%
4,70%
3,70%
17,00%
2,70%
15,00%
1,70%
0,70%
13,00%
-0,30%
21-Month Ahead Deviation from target (LHS)
12-Month-Ahead Inflation Forecast (LHS)
12-Month Ahead Inflation Target (LHS)
SELIC (RHS)
16/07/07
16/05/07
16/03/07
16/01/07
16/11/06
16/09/06
16/07/06
16/05/06
16/03/06
16/01/06
16/11/05
16/09/05
16/07/05
16/05/05
16/03/05
16/01/05
16/11/04
16/09/04
16/07/04
16/05/04
16/03/04
16/01/04
16/11/03
16/09/03
16/07/03
16/05/03
16/03/03
16/01/03
16/11/02
16/09/02
16/07/02
16/05/02
16/03/02
16/01/02
11,00%
16/11/01
-1,30%
12-Month ahead Inflation Forecast
Inflation (Last 12 Months)
mar/07
jan/07
nov/06
set/06
jul/06
mai/06
mar/06
jan/06
nov/05
set/05
jul/05
mai/05
mar/05
jan/05
nov/04
set/04
jul/04
mai/04
mar/04
jan/04
nov/03
set/03
jul/03
mai/03
mar/03
jan/03
nov/02
set/02
jul/02
mai/02
mar/02
jan/02
nov/01
(%)
Actual and Forecast Inflation
20
18
16
14
12
10
8
6
4
2
0
Inflation (12-Month Lag)
Forecast Inflation
mar/06
jan/06
nov/05
set/05
jul/05
mai/05
mar/05
jan/05
nov/04
set/04
jul/04
mai/04
mar/04
jan/04
nov/03
set/03
jul/03
mai/03
mar/03
jan/03
nov/02
set/02
jul/02
mai/02
mar/02
jan/02
nov/01
(%)
Lagged Actual and Forecast Inflation
20
18
16
14
12
10
8
6
4
2
0
CREDIBILIDADE
IPCA: Meta 12 meses à frente
13%
12%
11%
10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
Meta 12 meses
CREDIBILIDADE
IPCA: Meta e Expectativa 12 meses à frente
13%
12%
11%
10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
Expectativa FOCUS 12 meses
Expectativa 12 meses
-1%
-2%
Desvio Projetado da Meta de Inflação em 12 meses
Expectativa FOCUS 12 meses
Expectativa 12 meses
16/7/2007
16/5/2007
16/3/2007
16/1/2007
16/11/2006
16/9/2006
16/7/2006
16/5/2006
16/3/2006
16/1/2006
16/11/2005
16/9/2005
16/7/2005
16/5/2005
16/3/2005
16/1/2005
16/11/2004
16/9/2004
16/7/2004
16/5/2004
16/3/2004
16/1/2004
16/11/2003
16/9/2003
16/7/2003
16/5/2003
16/3/2003
16/1/2003
16/11/2002
16/9/2002
16/7/2002
16/5/2002
16/3/2002
16/1/2002
16/11/2001
CREDIBILIDADE
IPCA: Meta e Expectativa 12 meses à frente
13%
12%
11%
10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
-1%
-2%
Desvio Projetado da Meta de Inflação em 12 meses
Expectativa FOCUS 12 meses
Expectativa 12 meses
16/1/2007
16/11/2006
16/9/2006
16/7/2006
16/5/2006
16/3/2006
16/1/2006
16/11/2005
16/9/2005
16/7/2005
16/5/2005
16/3/2005
16/1/2005
16/11/2004
16/9/2004
16/7/2004
16/5/2004
16/3/2004
16/1/2004
16/11/2003
16/9/2003
16/7/2003
16/5/2003
16/3/2003
16/1/2003
16/11/2002
16/9/2002
16/7/2002
16/5/2002
16/3/2002
16/1/2002
16/11/2001
CREDIBILIDADE
Meta, Expectativa e Selic
13%
29,00%
12%
11%
27,00%
10%
9%
25,00%
8%
23,00%
7%
6%
21,00%
5%
4%
19,00%
3%
17,00%
2%
1%
15,00%
0%
Selic (RHS)
13,00%
11,00%
Desvio esperado da Meta
Surpresa Inflacionária [IPCAmês t-Emês t-1(IPCAmês t)] vs. Desvio
Projetado da Meta de Inflação
9%
4,5%
8%
4,0%
7%
3,5%
6%
3,0%
5%
2,5%
4%
2,0%
3%
1,5%
2%
1,0%
1%
0,5%
0%
0,0%
-1%
-0,5%
-2%
-1,0%
-3%
-1,5%
-4%
-2,0%
-5%
-2,5%
Desvio Projetado da Meta de Inflação em 12 meses
Surpresa Inflacionária = IPCA(t)-E[IPCA(t)/t-1]
Surpresa Inflacionária
CREDIBILIDADE
CREDIBILIDADE
Surpresa Inflacionária e Desvio Projetado da
Meta de Inflação
10.00%
8.00%
Desvio Esperado da Meta
6.00%
4.00%
2.00%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0.00%
0.50%
-2.00%
Surpresa Inflacionária = IPCA(t) - E[IPCA(t)/t-1]
1.00%
1.50%
2.00%
2.50%
CREDIBILIDADE
Desvios Projetados da Meta no Brasil
Variável dependente: Desvio da Meta 12 meses = E(IPCA 12 meses) - Meta 12 meses
Sample (adjusted):
2001M12 2005M04
C
DESVIO(-1)
SURPRESA
D(LOG(CAMBIO(-1)))
D(LOG(CAMBIO))
LOG(CAMBIO/CAMBIO(-4))
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
Sample (adjusted):
2002M01 2005M04
Sample (adjusted):
2001M12 2005M04
Sample (adjusted):
2002M03 2005M03
coefficient
p-value
coefficient
p-value
coefficient
p-value
coefficient
p-value
0.004157
0.529557
1.064562
-
0.0531
0.004141
0.535157
1.055992
0.005247
-
0.0629
0.004144
0.523073
1.128314
0.015109
-
0.0641
0.003791
0.585586
0.867386
0.022799
0.0958
0.669818
0.65244
0.009553
0.003468
134.0671
1.328888
0.013846
0.016205
-6.393516
-6.268133
38.54398
0
0.0007
0.0216
-
0.670546
0.643092
0.009802
0.003459
130.3583
1.376169
0.013788
0.016407
-6.317914
-6.149026
24.42391
0
0.0009
0.0270
0.8183
-
0.672638
0.645357
0.009752
0.003424
130.5612
1.301655
0.014013
0.016376
-6.328062
-6.159175
24.65663
0
0.0011
0.0200
0.5160
-
0.0004
0.0704
0.0555
0.705531
0.678761
0.009651
0.003074
121.3222
1.466368
0.0141
0.017027
-6.341742
-6.167589
26.3554
0
Onde,
Surpresa t (dia 15 do mês t)= IPCA mês t-1 (divulgado dia 15 do mês t) – Expectativa IPCA do mês t-1 (no dia 15 do mês t-1)
Robusto a variações da janela de estimação: coef da surpresa positivo e significativo com dados a partir de jan/2003, porém
menor.
O Efeito da surpresa inflacionária de curto
prazo nas expectativas de médio prazo: uma
comparação internacional
CHILE
Sample (adjusted):
2001M10 2004M10
Included observations:
37 after adjustments
BRAZIL
Sample (adjusted):
2002M01 2005M03
Included observations:
39 after adjustments
TURKEY
Sample (adjusted):
2001M10 2004M11
Included observations:
38 after adjustments
UK
Sample (adjusted):
1997M10 2003M12
Included observations:
75 after adjustments
MÉXICO
Sample (adjusted):
2001M06 2004M09
Included observations:
40 after adjustments
ISRAEL
Sample (adjusted):
1992M02 1996M01
Included observations:
48 after adjustments
coefficient
p-value
coefficient
p-value
coefficient
p-value
coefficient
p-value
coefficient
p-value
coefficient
p-value
0.9924
0.0369133
0.5213619
3.4282796
-1.7598839
0.8238
-0.9031188
-0.043515
8.8298457
-13.069899
0.0120
-0.0035178
-0.0017427
-1.2000911
1.5538316
0.8397
-0.0863169
-0.0515741
2.4876646
1.073216
0.0518
-0.1637156
0.1429904
15.979138
-3.5902507
0.3222
C
0.000275
D(INFLA(-1))
0.0633989
D(LOG(CAMBIO(-1)))
2.1412376
D(LOG(COMMODITIES(-1))) -1.6111915
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
0.3253
0.0271
0.0201
0.2562016
0.1885835
0.1651627
0.9001971
16.246373
1.4380691
-0.0162162
0.1833538
-0.6619661
-0.4878128
3.7889528
0.0193467
Erros-Padrão estimado pelo método Newey-West
0.1893343
0.1198486
0.7106161
17.674135
-39.905144
1.1456455
0.02
0.7574542
2.2515459
2.4221676
2.7247971
0.0589001
0.0223
0.1083
0.6275
0.1663264
0.0927669
1.462022
72.675279
-66.239552
1.3936799
-0.9473684
1.5349508
3.6968185
3.869196
2.2611154
0.0990347
0.8659
0.0592
0.1615
0.1012891
0.0633154
0.151313
1.6255883
37.265299
2.0751148
-0.004
0.1563434
-0.8870746
-0.7634753
2.6673491
0.0541899
0.9354
0.1280
0.1123
0.1053712
0.0308188
0.1849389
1.2312866
12.858856
1.832076
-0.0665
0.1878563
-0.4429428
-0.2740548
1.4133845
0.254742
0.4203
0.1579
0.3291
0.0834617
0.0209705
0.9144702
36.795256
-61.729079
1.110601
-0.070125
0.9242122
2.7387116
2.8946451
1.3355744
0.2749977
0.4923
0.0287
0.7343
PRE_252
IGP_252
5/01/2007
19/10/2006
8/08/2006
29/05/2006
15/03/2006
29/12/2005
18/10/2005
5/08/2005
27/05/2005
15/03/2005
29/12/2004
15/10/2004
4/08/2004
25/05/2004
12/03/2004
29/12/2003
16/10/2003
7/08/2003
28/05/2003
19/03/2003
6/01/2003
22/10/2002
13/08/2002
4/06/2002
22/03/2002
11/01/2002
29/10/2001
IGPM Mercado = (1+Pré)/(1+Cupom IGPM) -1
38%
36%
34%
32%
30%
28%
26%
24%
22%
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
-2%
-4%
-6%
PRE_252
IGP_252
IGPM Mercado 12 meses
5/01/2007
19/10/2006
8/08/2006
29/05/2006
15/03/2006
29/12/2005
18/10/2005
5/08/2005
27/05/2005
15/03/2005
29/12/2004
15/10/2004
4/08/2004
25/05/2004
12/03/2004
29/12/2003
16/10/2003
7/08/2003
28/05/2003
19/03/2003
6/01/2003
22/10/2002
13/08/2002
4/06/2002
22/03/2002
11/01/2002
29/10/2001
IGPM Mercado = (1+Pré)/(1+Cupom IGPM) -1
38%
36%
34%
32%
30%
28%
26%
24%
22%
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
-2%
-4%
-6%
IGPM Mercado vs. IGPM esperado FOCUS
40%
35%
30%
25%
20%
15%
10%
5%
0%
IGPM Mercado
E (IGPM 12 Meses) Focus
Prêmio de Risco IGPM =
IGPM “mercado” – E(IGPM) focus
40%
35%
30%
25%
20%
15%
10%
5%
0%
-5%
Prêmio de Risco IGPM
IGPM Mercado
E (IGPM 12 Meses) Focus
Surpresa IGPM [IGPMmês t-Emês t-1(IGPMmês t)] vs.
Medidas de Exp IGPM
40%
35%
30%
25%
20%
15%
10%
5%
0%
-5%
Prêmio de Risco IGPM
IGPM Mercado
E (IGPM 12 Meses) Focus
Surpresa IGPM 12 Meses
Surpresa IGPM vs. Prêmio de Risco IGPM
(quanto maior a incerteza, maior o prêmio de risco)
Surpresa IGPM vs. Premio de Risco IGPM
25.00
20.00
15.00
10.00
5.00
-2.00
-1.00
0.00
0.00
-5.00
-10.00
1.00
2.00
3.00
4.00
O efeito da surpresa IGPM nas diversas
medidas de expectativa
Sample (adjusted):
2002M01 2005M03
Sample (adjusted):
2002M01 2005M01
Sample (adjusted):
2002M01 2005M01
IGPM "Mercado"
E(IGPM) Focus
Premio de Risco
IGPM
coefficient
C
AR(1)
SURPRESAIGPM
D(LOG(CAMBIO(-1)))
5.146303
0.424787
3.508132
11.18084
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
0.72174
0.696444
3.67788
446.3844
-98.57058
1.848989
11.2927
6.67541
5.544356
5.718509
28.53139
0
p-value
0.0005
0.0016
0.0013
0.2363
coefficient
0.84885
0.845372
0.967997
3.204017
0.932032
0.926206
0.766057
20.5395
-42.83498
0.934948
7.782308
2.820002
2.401794
2.572415
159.9816
0
p-value
0.0306
0.0000
0.0000
0.1155
coefficient
2.129318
-0.070961
4.080157
12.40335
0.666897
0.636614
2.802954
259.2662
-88.51907
1.709882
3.42973
4.649777
5.001031
5.175184
22.02278
0
p-value
0.0011
0.6634
0.0001
0.0829
CREDIBILIDADE

Surpresas de curto prazo da inflação no Brasil (IPCA e IGPM) levam a
uma grande correção nas expectativas de médio prazo, mesmo
controlando para choques do câmbio.

Este efeito não foi encontrado nos outros países analisados.

Duas principais razões (e/ou):



Falta de credibilidade da autoridade monetária.
Excessiva indexação da economia.
O fato de o prêmio de risco inflacionário ser extremamente
correlacionado com a medida de surpresa inflacionária implica que,
pelo menos em parte, o problema se deve à falta de credibilidade da
autoridade monetária.
Can IT deliver sustained growth in
Brazil?
The five problematic points for IT in EM were:

1)
2)
3)
4)
5)
Fiscal dominance;
Financial dominance;
Low credibility;
Liability dollarization and currency substitution;
External dominance.
Brazil suffers from 1, although the CB has behaved as if it
did not. How long may this behavior continue without
compromising debt sustainability? 3 used to be a problem,
but it is solved as long as the “right” people are at the helm
of the CB (CB independence would greatly help). 5 is not
currently a problem given the massive, quickly and costly
dedollarization undertaken. 2 and 4 were never a problem
for Brazil.
Can IT deliver sustained growth in
Brazil?



Given the strong performance of the export sector,
external dominance seem to be a much smaller risk in
the medium run.
By fiercily pursuing the inflation target, the BCB has
achieved as much credibility as possible in the current
institutional arrangement. Granting instrument (not
goal) independence to the BCB would be a free lunch.
Fiscal sustainability in the medium and long runs,
despite the current high primary surplus, remain the
largest risk.
Can IT deliver sustained growth in
Brazil?



If nothing is done, large budget deficits will arise in the
future, mainly because of social security provisions
and demographics.
Brazil has a tax burden of 38% of GDP, by far the
largest in LA. The large (and poorly conceived) taxes
harm production and investment, thereby affecting
growth.
At the same time, government expenditures (which
are not affected by monetary policy) act as an
impediment to monetary contractions, requiring higher
real interest rates to affect aggregate demand.
Lessons for other Emerging Markets
IT can work in EM despite their fragilities;





The IT framework makes monetary policy more
transparent and, therefore, credible;
IT acts as better tool to align inflation expectations;
IT helps to achieve better economic policy in general,
by making clear who the true culprits of low growth and
unemployment are. In that sense, it helps to build the
institutional framework conducive to sustained growth,
as the fiscal responsibilty law.
IT must include escape clauses in face of large
external shocks, as the Brazilian provision to deal
with administered prices shocks and exchange rate
shocks.