On the Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach On the Effectiveness of Inflation Targeting: Overview Evidence from Semi/nonparametric Theoretical Context Dataset Approach The Impact of IT Treatment Effects of IT Logit ˆ π ( X i ) Weighting Omid M. Ardakani N. Kundan Kishor Suyong Song Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Semiparametric Results Department of Economics Concluding Remarks University of Wisconsin-Milwaukee March, 2015
On the Inflation Targeting framework Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Inflation targeting (IT) has become one of the most important monetary policy strategies. Overview Theoretical Context Dataset The Impact of IT Treatment Effects of IT Logit ˆ π ( X i ) Weighting Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Semiparametric Results Concluding Remarks
On the Inflation Targeting framework Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Inflation targeting (IT) has become one of the most important monetary policy strategies. Overview Theoretical Context What is Inflation Targeting? Dataset The Impact of IT Treatment Effects of IT Logit ˆ π ( X i ) Weighting Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Semiparametric Results Concluding Remarks
On the Inflation Targeting framework Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Inflation targeting (IT) has become one of the most important monetary policy strategies. Overview Theoretical Context What is Inflation Targeting? Dataset ◮ The public announcement of the target The Impact of IT ◮ Achieving the target over a medium to long horizon Treatment Effects of IT Logit ˆ π ( X i ) Weighting Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Semiparametric Results Concluding Remarks
On the Inflation Targeting framework Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Inflation targeting (IT) has become one of the most important monetary policy strategies. Overview Theoretical Context What is Inflation Targeting? Dataset ◮ The public announcement of the target The Impact of IT ◮ Achieving the target over a medium to long horizon Treatment Effects of IT Logit ˆ π ( X i ) Weighting The Reserve Bank of New Zealand initiated inflation Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) targeting in 1990. Semiparametric Results Concluding Remarks Another example of explicit inflation targeting is the United Kingdom. Federal Reserve’s implicit commitment to inflation targeting.
On the Inflation Targeting framework Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Overview 27 explicit inflation targeting countries in the world. Theoretical Context Dataset The Impact of IT Anchor inflationary expectations Treatment Effects of IT Logit ˆ π ( X i ) Build central banks credibility Weighting IT Goals Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Avoid business cycle fluctuations Semiparametric Results Concluding Remarks Increase transparency
On the Annual inflation rates and targets Effectiveness of Inflation Targeting: Evidence from (a) United Kingdom (b) Canada Semi/nonparametric Approach 12 12 Inflation Inflation Target Target 10 Overview 10 Theoretical Context 8 8 Inflation Inflation Dataset 6 6 4 The Impact of IT 4 2 Treatment Effects of IT 2 Logit ˆ π ( X i ) 0 Weighting 1980 1985 1990 1995 2000 2005 2010 1980 1985 1990 1995 2000 2005 2010 United Kingdom Canada Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) (c) Turkey (d) China Semiparametric Results Concluding Remarks Inflation 60 Target 20 50 15 40 Inflation Inflation 10 30 20 5 10 0 2000 2005 2010 1995 2000 2005 2010 2015 Turkey China
On the Relevant literature Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Overview The effectiveness of inflation targeting Theoretical Context Dataset Causal effect of Inflation Targeting The Impact of IT 1. The IT regime is successful Treatment Effects of IT Logit ˆ π ( X i ) (Mishkin and Schmidt-Hebbel (2001), Rose (2007), Weighting Filho (2011), Lucotte (2010)) Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Semiparametric Results 2. IT has no effect on the economy Concluding Remarks (Johnson (2002), Ball and Sheridan (2003), Lin and Ye (2007))
On the Problems with the existing literature Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Self-selection problem Targeters and non-targeters are different. Overview Theoretical Context Central banks’ decision to adopt inflation targeting is Dataset related to the benefits from the adoption of IT. The Impact of IT Treatment Effects of IT The difference between targeters and non-targeters is Logit ˆ π ( X i ) due to selection and not due to the IT regime. Weighting Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Semiparametric Results Concluding Remarks
On the Problems with the existing literature Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Self-selection problem Targeters and non-targeters are different. Overview Theoretical Context Central banks’ decision to adopt inflation targeting is Dataset related to the benefits from the adoption of IT. The Impact of IT Treatment Effects of IT The difference between targeters and non-targeters is Logit ˆ π ( X i ) due to selection and not due to the IT regime. Weighting Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Random assignment solves the selection problem. Semiparametric Results Concluding Remarks Effectiveness can be estimated using simple means between countries. Treatment effect: the terminology comes from medicine Randomization is not feasible in our case.
On the Problems with the existing literature Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Overview Two sets of countries are different. Theoretical Context Dataset It is difficult to compare them. The Impact of IT Treatment Effects of IT Logit ˆ π ( X i ) One solution is propensity score analysis Weighting Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Semiparametric Results Concluding Remarks
On the Problems with the existing literature Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Overview Two sets of countries are different. Theoretical Context Dataset It is difficult to compare them. The Impact of IT Treatment Effects of IT Logit ˆ π ( X i ) One solution is propensity score analysis Weighting Nonparametric ˆ π ( X i ) Propensity score is the probability of adopting IT. Semiparametric ˆ π ( X i ) Semiparametric Results Concluding Propensity score is a scalar variable. Remarks We can find countries with similar propensity score .
On the Problems with the existing literature Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Stages in propensity score analysis Overview 1. Estimating propensity score Theoretical Context We model the probability of IT using covariates. Dataset 2. Finding the effect of IT The Impact of IT Treatment Effects of IT We compare the difference between matches on the Logit ˆ π ( X i ) outcome measure of interest. Weighting Nonparametric ˆ π ( X i ) What is a model ? Can we trust our model? Semiparametric ˆ π ( X i ) Semiparametric Results Concluding What if the model is wrong. (Model Misspecification) Remarks Misspecified propensity score in the first stage leads us to inconsistent results in the second stage.
On the Contribution Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach 1. Estimate the effectiveness of IT taking into account the “ model misspecification ” Overview ◮ Nonparametric series propensity score Theoretical Context Dataset Overcoming problems with nonparametric estimation. The Impact of IT Treatment Effects of IT ◮ Proposing semiparametric single index propensity score Logit ˆ π ( X i ) Weighting 2. In the first stage we consider the role of preconditions Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) (financial development indicators) along with Semiparametric Results macroeconomic predictors (such as openness and Concluding Remarks money growth). 3. Examine the effectiveness of IT on inflation, inflation variability, fiscal discipline, sacrifice ratio, exchange rate volatility and interest rate variability.
On the Theoretical framework Effectiveness of Inflation Targeting: Evidence from Semi/nonparametric Approach Overview Theoretical Context Transparency increases the effectiveness of monetary Dataset policy (Svensson (1999) and Woodford (2005)). The Impact of IT Treatment Effects of IT The effectiveness of IT is considered through aggregate Logit ˆ π ( X i ) Weighting demand channel and inflation expectation channel. Nonparametric ˆ π ( X i ) Semiparametric ˆ π ( X i ) Semiparametric Results Monetary policy → aggregate demand → inflation Concluding Remarks Monetary policy → inflation expectations → inflation
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