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Inflation control around the world: Why are some countries more successful than others? The views are those of the author and do not necessarily reflect those of the Central Bank of Iceland Thórarinn G. Pétursson Central Bank of Iceland and Reykjavík University

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inflation control around the world why are some countries more successful than others

Inflation control around the world: Why are some countries more successful than others?

The views are those of the author and do not necessarily reflect those of the Central Bank of Iceland

Thórarinn G. Pétursson

Central Bank of Iceland and Reykjavík University

The 6th Norges Bank Monetary Policy Conference: Inflation Targeting Twenty Years On, Oslo 11-12 June 2009

two key questions
Two key questions
  • This paper attempts to address two key questions concerning inflation performance
    • Why does inflation tend to be more volatile in some countries than in others?
    • What explains the general decline in inflation volatility observed over the last two decades?
country sample inclusion criteria
Country sampleInclusion criteria
  • Focus on relatively developed, market-based economies
  • Countries similarly developed as OECD countries
    • Countries with PPP adjusted GDP per capita below the lowest OECD member (Turkey) are excluded
  • Countries of similar size as OECD countries
    • Countries with PPP adjusted GDP below the smallest OECD member (Iceland) are excluded
  • Final country sample of 42 countries
  • Estimation period 1985-2005
country sample two interesting country groups
Country sampleTwo interesting country groups
  • Combating inflation seems particularly difficult in two sub-groups
  • Very small, open economies (VSOE)
    • A sample of 7 countries with population below 2.5 million
  • Emerging market economies (EME)
    • A sample of 15 emerging market countries
country sample treatment and control groups
Country sampleTreatment and control groups
  • The non-inflation targeting group therefore includes a very heterogeneous group of countries
    • Ranging from very small to very large countries and from emerging market economies to very developed industrial countries
    • Wide array of monetary policy frameworks ranging from pegs, currency boards, monetary unions to floating rates with monetary targets or hybrid frameworks
  • Therefore offers a very interesting “control” group to test against the “treatment” group of IT countries
  • Country sample also includes a number of very small and reasonably developed countries that are usually excluded in this type of analysis
slide8
Question 1

Why does inflation tend to be more volatile in some countries than in others?

cross country analysis variables included
Cross-country analysisVariables included
  • Two measures of economic structure
    • Economic size and per capita income
  • Output volatility
  • Openness to trade
  • Two indicators of exposure to external shocks
    • Cyclical co-movement of domestic and world output and cyclical co-movement of private consumption and exchange rate
  • Two indicators of trade patterns
    • Trade diversification and the share of commodities in merchandise exports
  • Two measures of importance of exchange rate fluctuations
    • Volatility of the risk premium in multilateral exchange rates and level of exchange rate pass-through
  • Two indicators of monetary policy performance
    • Predictability of monetary policy and central bank independence
measuring exchange rate risk
Measuring exchange rate risk
  • Use a general signal-extraction approach suggested by Durlauf and Hall for rational expectations models
  • Models are a sum of two unobserved components
    • Combination of the data implied under the null hypothesis that the model is true
    • Combination of the data under the alternative: model noise
  • They show how this model noise can be extracted from the data and how a lower-bound of the variance of this noise component can be constructed
measuring exchange rate risk11
Measuring exchange rate risk
  • In the context of this paper I use the standard workhorse of exchange rate determination
    • Money market eq.
    • PPP condition
    • UIP condition
  • A time-varying risk premium has been added to the standard UIP condition
    • Can also be interpreted as the rational expectations deviation from the model – i.e. the non-fundamental part of exchange rate behaviour or model noise
measuring exchange rate risk12
Measuring exchange rate risk
  • This can be solved to give the standard present-value condition
  • Where
  • f are economic fundamentals and κ the present value of the current and expected risk premium (or noise)
measuring exchange rate risk13
Measuring exchange rate risk
  • By defining the perfect-foresight riskless exchange rate as
  • One can show that (v is the RE forecast error)
  • The variance of (s – s*) therefore gives a lower-bound estimate of true variance of κ
cross country analysis robustness
Cross-country analysisRobustness
  • The cross-country results are found robust to a number of alterations in model setup and estimation approach
    • Allowing for dummy variables for different country sub-groups
    • Possible heteroscedasticity
    • Changes in country sample
    • Using robust estimators
    • Instrumental variables estimation
slide17
Question 2

What explains the general decline in inflation volatility observed over the last two decades?

panel analysis
Panel analysis
  • To address the second question, I use a panel framework with a treatment and a control group
    • Introduce a regime dummy for IT adoption
    • Include the three variables found important for explaining the cross-country variation in inflation volatility
  • Following the previous literature, I use the average IT adoption date as the break-date for the non-targeting countries (1996Q4)
  • Time variation in the data
    • Rolling two-year standard deviations of INFVOL, EXRISK and POLICY
    • Following Edwards (2007), I use a simple regression allowing for break in pass-through at regime change dates
panel analysis specification
Panel analysisSpecification
  • Fixed country and time specific effects (allowing for random effects did not change results)
  • D = IT dummy variable equal 1 from first quarter after IT adoption and 0 otherwise
  • Z = the 3 variables from cross-country analysis (EXRISK, PASS and POLICY)
  • Estimated for 2 control groups: all 25 non-IT countries and 17 industrial non-IT countries
panel analysis instrumental variables estimation
Panel analysisInstrumental variables estimation
  • IT adoption is likely to be an endogenous decision based inter alia in past inflation performance
  • To account for this, the panel is estimated with IV using as instruments
    • Average pre-targeting (or pre-1997 for non-targeters) average inflation
    • Lagged IT dummy, lagged INFVOL and lagged Z controls
panel analysis main results
Panel analysisMain results
  • IT has played a significant role in observed improvement in inflation performance over the last two decades
  • The three controls found important in explaining the cross-country variation in inflation volatility are also found important in explaining the time variation in inflation volatility
    • The general decline in inflation volatility can in part be explained by the general increase in monetary policy transparency and the decline in exchange rate pass-through
    • The volatility of exchange rate risk premium has increased in the EMEs and VSOEs at the same time it has fallen in the larger and more developed countries
    • This can at least partially explain why the EMEs and VSOEs continue to be relatively less successful in stabilising inflation