Prof. Márcio Garcia

Macro II, Tópico 1: Política Monetária e Fiscal: Inflation Targeting em Mercados Emergentes (14 de agosto de 2007, 16h) 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 CountriesIsrael 1997 : Q2 Y 8.5 1 - 3 Y YCzech Republic 1998 : Q1 Y 13.1 3 (+/- 1) Y YKorea 1998 :Q2 Y 3.2 2.5 - 3.5 Y YPoland 1999 : Q1 Y 9.9 2.5 (+/- 1) Y YBrazil 1999 : Q2 Y 3.3 4.5 (+/- 2) Y YChile 1999 : Q3 Y 2.9 2 - 3 Y YColombia 1999 : Q3 Y 9.3 3.5 - 4.5 Y YSouth Africa 2000 : Q1 Y 2.3 3 - 6 Y YThailand 2000 : Q2 Y 1.7 1.5 - 2.5 Y YMexico 2001 : Q1 Y 8.1 3 Y YHungary 2001 : Q3 Y 10.5 3 (+/- 1) Y YPeru 2002 : Q1 Y -0.8 2 (+/- 1) Y YPhilippines 2002 : Q1 Y 3.8 4-5 Y YSlovak Rep. 2005:Q1 Y 3.2 3,5(+/-1) Y YIndonesia 2005:Q3 Y 7.8 5,5 (+/-1) Y YRomania 2005:Q3 Y 8.8 7,5(+/- 1) Y Y

Industrial CountriesNew Zealand 1990 : Q1 Y 7.0 1 -3 Y YCanada 1991 : Q1 Y 6.2 1 - 3 Y YUnited Kingdom 1992 : Q4 Y 3.6 2 Y YAustralia 1993 : Q1 Y 1.9 2 - 3 Y YSweden 1993 : Q1 Y 4.8 2 (+/- 1) Y YIceland 2001 : Q1 Y 3.9 2.5 Y YNorway 2001: Q1 Y 3.7 2.5 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







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 BrasilYEAR SETTING DATE CPI INFLATION GDP GROWTH

1999 30/6/1999 8,00% ±2,00% 8,94% 0,25%2000 30/6/1999 6,00% ±2,00% 5,97% 4,31%2001 30/6/1999 4,00% ±2,00% 7,67% 1,31%2002 28/6/2000 3,50% ±2,00% 12,56% 2,66%2003 28/6/2001 3,25% ±2,00% - - 2003* 27/6/2002 4,00% ±2,50% - - 2003* 21/1/2003 8,50% ±2,50% 9,30% 1,15%2004 37/6/2002 3,75% ±2,50% - - 2004* 25/6/2003 5,50% ±2,50% 7,60% 5,71%2005 25/6/2003 4,50% ±2,50% - - 2005** 23/9/2003 5,10% - 5,69% 2,94%2006 30/6/2004 4,50% ±2,00% 3,14% 3,70%2007 22/6/2005 4,50% ±2,00% 3,6%*** 4,3%***2008 22/6/2006 4,50% ±2,00% 3,89%*** 4,18%***2009 26/6/2007 4,50% ±2,00% 3,98%*** 4,15%***

* Revised Targets** Objective*** Consensus Forecasts (means) on June 22, 2007

Source: Brazilian Central Bank Web Page (www.bcb.gov.br)

TARGET

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.

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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.

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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:Previsão de Crescimento do PIB


O modelo básico de IT do BCB:Aderência


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Nominal and Real Exchange Rates and CPI Inflation

Cpi Inflation (Last 12 Months- LHS) Nominal Exchange Rate (BRL/USD) - RHS Real Exchange Rate - RHS

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Real and Nominal Interest Rate and CPI Inflation

CPI Inflation Nominal Interest Rate (SELIC)

Real Interest Rate (annualized) Real Interest Rate (with last 12 month inflation - annualized rate)

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Country - Risk: EMBI+ e EMBI+ BRAZIL

EMBI+ BRAZIL SPREAD EMBI+ SPREAD

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NET Public Debt (% of GDP) Primary Fiscal Balance (Last 12 Months - % os GDP)

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Inflation Expectations, target and Interest Rate

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12-Month Ahead Inflation Target (LHS) SELIC (RHS)

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12-Month ahead Inflation Forecast Inflation (Last 12 Months)

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Inflation (12-Month Lag) Forecast Inflation

CREDIBILIDADEIPCA: Meta 12 meses à frente

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CREDIBILIDADEIPCA: Meta e Expectativa 12 meses à frente

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Expectativa FOCUS 12 meses Expectativa 12 meses

CREDIBILIDADEIPCA: Meta e Expectativa 12 meses à frente

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Desvio Projetado da Meta de Inflação em 12 meses Expectativa FOCUS 12 meses Expectativa 12 meses

CREDIBILIDADEMeta, Expectativa e Selic

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Desvio Projetado da Meta de Inflação em 12 meses Expectativa FOCUS 12 meses Expectativa 12 meses Selic (RHS)

CREDIBILIDADESurpresa Inflacionária [IPCAmês t-Emês t-1(IPCAmês t)] vs. Desvio Projetado da Meta de Inflação

-2,5%

-2,0%

-1,5%

-1,0%

-0,5%

0,0%

0,5%

1,0%

1,5%

2,0%

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Desvio Projetado da Meta de Inflação em 12 meses Surpresa Inflacionária = IPCA(t)-E[IPCA(t)/t-1]

CREDIBILIDADESurpresa Inflacionária e Desvio Projetado da Meta de Inflação

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

-2.00% -1.50% -1.00% -0.50% 0.00% 0.50% 1.00% 1.50% 2.00% 2.50%

Surpresa Inflacionária = IPCA(t) - E[IPCA(t)/t-1]

De

svio

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CREDIBILIDADEDesvios 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.

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

C 0.004157 0.0531 0.004141 0.0629 0.004144 0.0641 0.003791 0.0958

DESVIO(-1) 0.529557 0.0007 0.535157 0.0009 0.523073 0.0011 0.585586 0.0004

SURPRESA 1.064562 0.0216 1.055992 0.0270 1.128314 0.0200 0.867386 0.0704

D(LOG(CAMBIO(-1))) - - 0.005247 0.8183 - - - -

D(LOG(CAMBIO)) - - - - 0.015109 0.5160 - -

LOG(CAMBIO/CAMBIO(-4)) - - - - - - 0.022799 0.0555

R-squared 0.669818 0.670546 0.672638 0.705531Adjusted R-squared 0.65244 0.643092 0.645357 0.678761S.E. of regression 0.009553 0.009802 0.009752 0.009651Sum squared resid 0.003468 0.003459 0.003424 0.003074Log likelihood 134.0671 130.3583 130.5612 121.3222Durbin-Watson stat 1.328888 1.376169 1.301655 1.466368Mean dependent var 0.013846 0.013788 0.014013 0.0141S.D. dependent var 0.016205 0.016407 0.016376 0.017027Akaike info criterion -6.393516 -6.317914 -6.328062 -6.341742Schwarz criterion -6.268133 -6.149026 -6.159175 -6.167589F-statistic 38.54398 24.42391 24.65663 26.3554Prob(F-statistic) 0 0 0 0

Variável dependente: Desvio da Meta 12 meses = E(IPCA 12 meses) - Meta 12 meses

Sample (adjusted): 2001M12 2005M04




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 0.0834617Adjusted R-squared 0.1885835 0.1198486 0.0927669 0.0633154 0.0308188 0.0209705S.E. of regression 0.1651627 0.7106161 1.462022 0.151313 0.1849389 0.9144702Sum squared resid 0.9001971 17.674135 72.675279 1.6255883 1.2312866 36.795256Log likelihood 16.246373 -39.905144 -66.239552 37.265299 12.858856 -61.729079Durbin-Watson stat 1.4380691 1.1456455 1.3936799 2.0751148 1.832076 1.110601Mean dependent var -0.0162162 0.02 -0.9473684 -0.004 -0.0665 -0.070125S.D. dependent var 0.1833538 0.7574542 1.5349508 0.1563434 0.1878563 0.9242122Akaike info criterion -0.6619661 2.2515459 3.6968185 -0.8870746 -0.4429428 2.7387116Schwarz criterion -0.4878128 2.4221676 3.869196 -0.7634753 -0.2740548 2.8946451F-statistic 3.7889528 2.7247971 2.2611154 2.6673491 1.4133845 1.3355744Prob(F-statistic) 0.0193467 0.0589001 0.0990347 0.0541899 0.254742 0.2749977

Erros-Padrão estimado pelo método Newey-West

MÉXICOSample (adjusted): 2001M06 2004M09

Included observations: 40 after adjustments

ISRAELSample (adjusted): 1992M02 1996M01


TURKEY UKSample (adjusted): 2001M10 2004M11




CHILESample (adjusted): 2001M10 2004M10


BRAZILSample (adjusted): 2002M01 2005M03


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

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

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PRE_252 IGP_252 IGPM Mercado 12 meses

IGPM Mercado vs. IGPM esperado FOCUS

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Surpresa IGPM [IGPMmês t-Emês t-1(IGPMmês t)] vs. Medidas de Exp IGPM

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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 0.666897Adjusted R-squared 0.696444 0.926206 0.636614S.E. of regression 3.67788 0.766057 2.802954Sum squared resid 446.3844 20.5395 259.2662Log likelihood -98.57058 -42.83498 -88.51907Durbin-Watson stat 1.848989 0.934948 1.709882Mean dependent var 11.2927 7.782308 3.42973S.D. dependent var 6.67541 2.820002 4.649777Akaike info criterion 5.544356 2.401794 5.001031Schwarz criterion 5.718509 2.572415 5.175184F-statistic 28.53139 159.9816 22.02278Prob(F-statistic) 0 0 0


Premio de Risco IGPM


E(IGPM) Focus


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.


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.

Documents

Prof. Márcio Garcia