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Seminário de Política Econômica Tópico 1 Política Monetária: Inflation Targeting em Mercados Emergentes (2 de março de 2012, 7h) Prof. Márcio Garcia

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Seminário de Política Econômica

Tópico 1

Política Monetária: Inflation

Targeting em Mercados Emergentes

(2 de março de 2012, 7h)

Prof. Márcio Garcia

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.

Política Monetária e Fiscal: Inflation

Targeting em Mercados Emergentes

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 1997 : Q2 Y 8.5 1 - 3 Y Y

Czech Republic 1998 : Q1 Y 13.1 3 (+/- 1) Y Y

Korea 1998 :Q2 Y 3.2 2.5 - 3.5 Y Y

Poland 1999 : Q1 Y 9.9 2.5 (+/- 1) Y Y

Brazil 1999 : Q2 Y 3.3 4.5 (+/- 2) Y Y

Chile 1999 : Q3 Y 2.9 2 - 3 Y Y

Colombia 1999 : Q3 Y 9.3 3.5 - 4.5 Y Y

South Africa 2000 : Q1 Y 2.3 3 - 6 Y Y

Thailand 2000 : Q2 Y 1.7 1.5 - 2.5 Y Y

Mexico 2001 : Q1 Y 8.1 3 Y Y

Hungary 2001 : Q3 Y 10.5 3 (+/- 1) Y Y

Peru 2002 : Q1 Y -0.8 2 (+/- 1) Y Y

Philippines 2002 : Q1 Y 3.8 4-5 Y Y

Slovak Rep. 2005:Q1 Y 3.2 3,5(+/-1) Y Y

Indonesia 2005:Q3 Y 7.8 5,5 (+/-1) Y Y

Romania 2005:Q3 Y 8.8 7,5(+/- 1) Y Y

Industrial Countries

New Zealand 1990 : Q1 Y 7.0 1 -3 Y Y

Canada 1991 : Q1 Y 6.2 1 - 3 Y Y

United Kingdom 1992 : Q4 Y 3.6 2 Y Y

Australia 1993 : Q1 Y 1.9 2 - 3 Y Y

Sweden 1993 : Q1 Y 4.8 2 (+/- 1) Y Y

Iceland 2001 : Q1 Y 3.9 2.5 Y Y

Norway 2001: Q1 Y 3.7 2.5 Y Y

Source: National Authorities

Países com Meta de Inflação

Bank of England

Bank of England

Decisões em Bancos Centrais com Meta de Inflação

Bank of England

Bank of England

Modelos e Previsões

Bank of England

Bank of England

Accountability e Transparência

Bank of England

Bank of England

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

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.

[1] Bogdanski, J., A. Tombini e S. Werlang (2000) “Implementing Inflation Targeting in Brazil”, BCB Working Paper Series nº 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.

)ln( 14131210 ttttt ICshdh

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 (pass-

through). 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.

tttttttttheeee

25214133221)()(~~~

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

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:

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

-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

35%

40%

45%

50%

55%

60%

65%

Balanço fiscal primário (%PIB)

Dívida Pública Líquida (% PIB) Balanço Fiscal primário (últimos 12 meses %PIB)

2%

4%

6%

8%

10%

12%

14%

16%

18%

IPCA - Acumulado 12 meses Expectativa IPCA (12 meses) - Último dia do Mês

Inflação Esperada x Inflação Observada

2%

4%

6%

8%

10%

12%

14%

16%

18%

Expectativa IPCA (12 meses) - Último dia do Mês - Adiantada 12 meses IPCA - Acumulado 12 meses

Inflação Esperada (12 meses a frente) x Inflação Observada

CREDIBILIDADE

IPCA: Meta 12 meses à frente

CREDIBILIDADE

IPCA: Meta e Expectativa 12 meses à frente

CREDIBILIDADE

IPCA: Meta e Expectativa 12 meses à frente

CREDIBILIDADE

Meta, Expectativa e Selic

CREDIBILIDADE Surpresa Inflacionária [IPCAmês t-Emês t-1(IPCAmês t)] vs. Desvio

Projetado da Meta de Inflação

CREDIBILIDADE

Surpresa Inflacionária e Desvio Projetado da

Meta de Inflação

CREDIBILIDADE

Desvios Projetados da Meta no Brasil

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

coefficient p-value coefficient p-value coefficient p-value coefficient p-value coefficient p-value coefficient p-value

C 0.000275 0.9924 0.0369133 0.8238 -0.9031188 0.0120 -0.0035178 0.8397 -0.0863169 0.0518 -0.1637156 0.3222

D(INFLA(-1)) 0.0633989 0.3253 0.5213619 0.0223 -0.043515 0.8659 -0.0017427 0.9354 -0.0515741 0.4203 0.1429904 0.4923

D(LOG(CAMBIO(-1))) 2.1412376 0.0271 3.4282796 0.1083 8.8298457 0.0592 -1.2000911 0.1280 2.4876646 0.1579 15.979138 0.0287

D(LOG(COMMODITIES(-1))) -1.6111915 0.0201 -1.7598839 0.6275 -13.069899 0.1615 1.5538316 0.1123 1.073216 0.3291 -3.5902507 0.7343

R-squared 0.2562016 0.1893343 0.1663264 0.1012891 0.1053712 0.0834617

Adjusted R-squared 0.1885835 0.1198486 0.0927669 0.0633154 0.0308188 0.0209705

S.E. of regression 0.1651627 0.7106161 1.462022 0.151313 0.1849389 0.9144702

Sum squared resid 0.9001971 17.674135 72.675279 1.6255883 1.2312866 36.795256

Log likelihood 16.246373 -39.905144 -66.239552 37.265299 12.858856 -61.729079

Durbin-Watson stat 1.4380691 1.1456455 1.3936799 2.0751148 1.832076 1.110601

Mean dependent var -0.0162162 0.02 -0.9473684 -0.004 -0.0665 -0.070125

S.D. dependent var 0.1833538 0.7574542 1.5349508 0.1563434 0.1878563 0.9242122

Akaike info criterion -0.6619661 2.2515459 3.6968185 -0.8870746 -0.4429428 2.7387116

Schwarz criterion -0.4878128 2.4221676 3.869196 -0.7634753 -0.2740548 2.8946451

F-statistic 3.7889528 2.7247971 2.2611154 2.6673491 1.4133845 1.3355744

Prob(F-statistic) 0.0193467 0.0589001 0.0990347 0.0541899 0.254742 0.2749977

Erros-Padrão estimado pelo método Newey-West

MÉXICO

Sample (adjusted):

2001M06 2004M09

Included observations:

40 after adjustments

ISRAEL

Sample (adjusted):

1992M02 1996M01

Included observations:

48 after adjustments

TURKEY UK

Sample (adjusted):

2001M10 2004M11

Included observations:

38 after adjustments

Sample (adjusted):

1997M10 2003M12

Included observations:

75 after adjustments

CHILE

Sample (adjusted):

2001M10 2004M10

Included observations:

37 after adjustments

BRAZIL

Sample (adjusted):

2002M01 2005M03

Included observations:

39 after adjustments

IGPM Mercado = (1+Pré)/(1+Cupom IGPM) -1

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

22%

24%

26%

28%

30%

32%

34%

36%

38%

29

/1

0/

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27

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7/

5/

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3

26

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14

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20

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1/

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3

14

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04

5/

3/

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27

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04

16

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04

4/

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4

23

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16

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1/

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4

6/

1/

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29

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ar/

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08

1 year Nominal Rate 1 year Real Rate (IGPM)

IGPM Mercado = (1+Pré)/(1+Cupom IGPM) -1

IGPM Mercado vs. IGPM esperado FOCUS

0%

5%

10%

15%

20%

25%

30%

35%

40%

IGPM Mercado E (IGPM 12 Meses) Focus

Prêmio de Risco IGPM =

IGPM “mercado” – E(IGPM) focus

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

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

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

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)

y = 1,0297x - 0,0101R² = 0,299

-3

-2

-1

0

1

2

3

4

5

-1,50 -1,00 -0,50 0,00 0,50 1,00 1,50Infl

ati

on

Ris

k P

rem

ium

Inflation Surprise - IGPM(t)

d(IGPM Mercado)

Linear (d(IGPM Mercado))

Linear (d(IGPM Mercado))

O efeito da surpresa IGPM nas diversas

medidas de expectativa

coefficient p-value coefficient p-value coefficient p-value

C 5.146303 0.0005 0.84885 0.0306 2.129318 0.0011

AR(1) 0.424787 0.0016 0.845372 0.0000 -0.070961 0.6634

SURPRESAIGPM 3.508132 0.0013 0.967997 0.0000 4.080157 0.0001

D(LOG(CAMBIO(-1))) 11.18084 0.2363 3.204017 0.1155 12.40335 0.0829

R-squared 0.72174 0.932032 0.666897

Adjusted R-squared 0.696444 0.926206 0.636614

S.E. of regression 3.67788 0.766057 2.802954

Sum squared resid 446.3844 20.5395 259.2662

Log likelihood -98.57058 -42.83498 -88.51907

Durbin-Watson stat 1.848989 0.934948 1.709882

Mean dependent var 11.2927 7.782308 3.42973

S.D. dependent var 6.67541 2.820002 4.649777

Akaike info criterion 5.544356 2.401794 5.001031

Schwarz criterion 5.718509 2.572415 5.175184

F-statistic 28.53139 159.9816 22.02278

Prob(F-statistic) 0 0 0

Sample (adjusted):

2002M01 2005M01

Premio de Risco

IGPM

Sample (adjusted):

2002M01 2005M03

E(IGPM) Focus

Sample (adjusted):

2002M01 2005M01

IGPM "Mercado"

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) Fiscal dominance;

2) Financial dominance;

3) Low credibility;

4) Liability dollarization and currency substitution;

5) 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.

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.

Can IT deliver sustained growth in

Brazil?

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.