Inflation Forecasts in the Long Run Jamus J. Lim ∗ ∗ ESSEC Business School Jun 18, 2019 International Symposium on Forecasting 1/22 Lim Long-Run Inflation Forecasts
Introduction Motivation Theory Objective Empirics Contribution Results Related Literature Conclusion The Challenge of Long-Run Inflation Forecasting Median annual inflation in global panels is about 48% This is a full order of magnitude larger than mean annual growth rates ( ≈ 4 % ) Volatility of annual inflation is also massive 10,000% versus 7% for growth Sargent-Wallace further underscores the indeterminacy of prices Why would anyone wish to forecast inflation, especially in the long run? 2/22 Lim Long-Run Inflation Forecasts
Introduction Motivation Theory Objective Empirics Contribution Results Related Literature Conclusion The Challenge of Long-Run Inflation Forecasting Median annual inflation in global panels is about 48% This is a full order of magnitude larger than mean annual growth rates ( ≈ 4 % ) Volatility of annual inflation is also massive 10,000% versus 7% for growth Sargent-Wallace further underscores the indeterminacy of prices Why would anyone wish to forecast inflation, especially in the long run? 2/22 Lim Long-Run Inflation Forecasts
Introduction Motivation Theory Objective Empirics Contribution Results Related Literature Conclusion The Challenge of Long-Run Inflation Forecasting Median annual inflation in global panels is about 48% This is a full order of magnitude larger than mean annual growth rates ( ≈ 4 % ) Volatility of annual inflation is also massive 10,000% versus 7% for growth Sargent-Wallace further underscores the indeterminacy of prices Why would anyone wish to forecast inflation, especially in the long run? 2/22 Lim Long-Run Inflation Forecasts
Introduction Motivation Theory Objective Empirics Contribution Results Related Literature Conclusion The Necessity of Long-Term Forecasts Central banks worldwide are implicitly or explicitly required to perform such forecasts Inflation-targeting (> 60 worldwide) implies an commitment to realizing a numerical inflation outcome Even non-IT central banks routinely think about long-run inflation trends Private sector agents are also called on to generate long-run projections Expectations react to inflation targets as stable focal points Needed to guide investment decisions for agents with long-run liabilities (pensions, SWFs) 3/22 Lim Long-Run Inflation Forecasts
Introduction Motivation Theory Objective Empirics Contribution Results Related Literature Conclusion Which Forecasts Should We Use? Does inflation targeting induce superior long-term inflation forecasts? Or do projections derived from models, markets, or surveys do better? Perform a forecasting horse-race for inflation over the long run Compare models, markets, and surveys Benchmark forecasts to standard random walk but also to deviations from declared target 4/22 Lim Long-Run Inflation Forecasts
Introduction Motivation Theory Objective Empirics Contribution Results Related Literature Conclusion Which Forecasts Should We Use? Does inflation targeting induce superior long-term inflation forecasts? Or do projections derived from models, markets, or surveys do better? Perform a forecasting horse-race for inflation over the long run Compare models, markets, and surveys Benchmark forecasts to standard random walk but also to deviations from declared target 4/22 Lim Long-Run Inflation Forecasts
Introduction Motivation Theory Objective Empirics Contribution Results Related Literature Conclusion Evaluate Long-Run Inflation Forecasts Relative to Targets Consider inflation targets as an alternative benchmark to random walk or autoregressive model Compare the long-run (forecast windows ≥ 5 years) predictive performance instead of shorter-run headline or trend/core inflation Broad coverage across economies (DM and EM) with multiple overlapping forecast vintages allows examination of forecast performance variations over time Consider wide range of forecasts, including several families of (econometric) model projections, (implied) market forecasts, (expert) survey expectations, static anchors 5/22 Lim Long-Run Inflation Forecasts
Introduction Motivation Theory Objective Empirics Contribution Results Related Literature Conclusion Long-Run Inflation Forecasting Not Well Explored I Same spirit as rules-versus-discretion debate (Kydland & Prescott 1977; Taylor 1993) IT as more-flexible form of instrument rule (Svensson 1999) Most papers focus on relative economic performance under different rules, not inflation forecasting Papers that consider forecast performance (Diron & Mojon 2008; Lee 2012; Svensson 1997) are limited to short-term forecasts for a small number of countries Forecast evaluation literature (Ang, Bekaert & Wei 2007; Clark & Doh 2014; Faust & Wright 2013) Many papers examine panel of industrialized economies Diverse panels are comparatively fewer in number, and even if time horizons are long, forecast windows tend to be short (< 3 years) 6/22 Lim Long-Run Inflation Forecasts
Introduction Motivation Theory Objective Empirics Contribution Results Related Literature Conclusion Long-Run Inflation Forecasting Not Well Explored II Trend/global inflation forecasting (Cogley 2002; Stock & Watson 2016/Ciccarelli & Mojon 2010; Parker 2018) Unobserved trend can be interpreted as long-term inflation expectation Alternatively, long-run inflation may be attributable to common (unobserved) global factor Forecast horizons are conservative (8–20 quarters), leaving open question of whether such trend estimates reliably forecast long-run inflation Evaluation of inflation targeting regimes (Johnson 2002, 2003; Lin & Ye 2007; Ardakani & Kishor 2018) Most studies limited to advanced economies Focus only on relative success of banks in achieving targets, not on forecasts 7/22 Lim Long-Run Inflation Forecasts
Introduction Theory Empirics Results Conclusion Families of Inflation Models I Autoregressive Moving Average Models Best short-run track record combined with univariate simplicity (e.g. Ang, Bekaert & Wei 2007; Faust & Wright 2013) J K � � π ARMA ˆ = φ ij π i , t − j + ε it + θ ik ε i , t − k it j = 1 k = 1 AR(1) with dynamic/recursive forecasts Robust OLS estimation of fixed forward regression forecasts ARMA(1,1) with dynamic/recursive forecasts Robust OLS estimation of fixed forward regression forecasts with Prais-Winsten serial correlation correction 8/22 Lim Long-Run Inflation Forecasts
Introduction Theory Empirics Results Conclusion Families of Inflation Models I Autoregressive Moving Average Models Best short-run track record combined with univariate simplicity (e.g. Ang, Bekaert & Wei 2007; Faust & Wright 2013) J K � � π ARMA ˆ = φ ij π i , t − j + ε it + θ ik ε i , t − k it j = 1 k = 1 AR(1) with dynamic/recursive forecasts Robust OLS estimation of fixed forward regression forecasts ARMA(1,1) with dynamic/recursive forecasts Robust OLS estimation of fixed forward regression forecasts with Prais-Winsten serial correlation correction 8/22 Lim Long-Run Inflation Forecasts
Introduction Theory Empirics Results Conclusion Families of Inflation Models II New Keynesian Phillip Curve Models Preferred forecasting approach of central banks (e.g. Rudd & Whelan 2007) π PC = π e it + γ i ( u it − ˜ ˆ u i ) + ε it it Robust OLS estimation of pseudo out-of-sample forecasts with recursive window (adaptive expectations) Robust OLS estimation of fixed forward regression forecasts (adaptive) GMM IV estimation of pseudo out-of-sample forecasts with recursive window (rational expectations) GMM IV estimation of fixed forward regression forecasts (rational) 9/22 Lim Long-Run Inflation Forecasts
Introduction Theory Empirics Results Conclusion Families of Inflation Models II New Keynesian Phillip Curve Models Preferred forecasting approach of central banks (e.g. Rudd & Whelan 2007) π PC = π e it + γ i ( u it − ˜ ˆ u i ) + ε it it Robust OLS estimation of pseudo out-of-sample forecasts with recursive window (adaptive expectations) Robust OLS estimation of fixed forward regression forecasts (adaptive) GMM IV estimation of pseudo out-of-sample forecasts with recursive window (rational expectations) GMM IV estimation of fixed forward regression forecasts (rational) 9/22 Lim Long-Run Inflation Forecasts
Introduction Theory Empirics Results Conclusion Families of Inflation Models III Vector Autoregression Models Model-agnostic approach that allows feedback effects and gap transforms (Webb 1995) X it = Φ X i , t − 1 + ε it ∆ X it = Φ ∆ X i , t − 1 + ε it VAR estimation of dynamic/recursive forecasts (levels) Robust ARDL estimation of fixed forward regression forecasts (levels) VAR estimation of dynamic/recursive forecasts (differences) Robust ARDL estimation of fixed forward regression forecasts (differences) 10/22 Lim Long-Run Inflation Forecasts
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