Real-Time Inflation Forecasting in a Changing World

This paper revisits inflation forecasting using reduced-form Phillips curve forecasts, that is, inflation forecasts that use activity and expectations variables. We propose a Phillips-curve-type model that results from averaging across different regression specifications selected from a set of potential predictors. The set of predictors includes lagged values of inflation, a host of real-activity data, term structure data, nominal data, and surveys. In each individual specification, we allow for stochastic breaks in regression parameters, where the breaks are described as occasional shocks of random magnitude. As such, our framework simultaneously addresses structural change and model uncertainty that unavoidably affect Phillips-curve-based predictions. We use this framework to describe personal consumption expenditure (PCE) deflator and GDP deflator inflation rates for the United States in the post-World War II period. Over the full 1960-2008 sample, the framework indicates several structural breaks across different combinations of activity measures. These breaks often coincide with policy regime changes and oil price shocks, among other important events. In contrast to many previous studies, we find less evidence of autonomous variance breaks and inflation gap persistence. Through a real-time out-of-sample forecasting exercise, we show that our model specification generally provides superior one-quarter-ahead and one-year-ahead forecasts for quarterly inflation relative to an extended range of forecasting models that are typically used in the literature.

[1]  J. Stock,et al.  A Comparison of Direct and Iterated Multistep Ar Methods for Forecasting Macroeconomic Time Series , 2005 .

[2]  Giorgio E. Primiceri,et al.  The Time Varying Volatility of Macroeconomic Fluctuations , 2006 .

[3]  T. Gneiting.  Making and Evaluating Point Forecasts , 2009, 0912.0902.

[4]  Robert Kohn,et al.  Semiparametric Bayesian Inference for Time Series with Mixed Spectra , 1996 .

[5]  Min Wei,et al.  What Does the Yield Curve Tell Us About GDP Growth? , 2003 .

[6]  James H. Stock,et al.  [Evolving Post-World War II U.S. Inflation Dynamics]: Comment , 2001, NBER Macroeconomics Annual.

[7]  Shaun P. Vahey,et al.  Combining forecast densities from VARs with uncertain instabilities , 2010 .

[8]  John Geweke,et al.  Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments , 1991 .

[9]  R. Kohn,et al.  On Gibbs sampling for state space models , 1994 .

[10]  J. Stock,et al.  Efficient Tests for an Autoregressive Unit Root , 1992 .

[11]  J. Galí,et al.  Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory , 1998 .

[12]  Jan J. J. Groen,et al.  Investigating the Structural Stability of the Phillips Curve Relationship , 2008 .

[13]  Todd E. Clark,et al.  Decomposing the Declining Volatility of Long-Term Inflation Expectations , 2009 .

[14]  Peter E. Rossi,et al.  Bayesian Analysis of Stochastic Volatility Models , 1994 .

[15]  Simon van Norden,et al.  Série Scientifique Scientific Series the Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time the Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time* , 2022 .

[16]  Sveriges Riksbank.  Efficient Bayesian inference for multiple change-point and mixture innovation models , 2006 .

[17]  Jonathan H. Wright,et al.  Forecasting Inflation , 2011 .

[18]  J. Stock,et al.  Macroeconomic Forecasting Using Diffusion Indexes , 2002 .

[19]  Mark W. Watson,et al.  Has inflation become harder to forecast , 2005 .

[20]  J. Stock,et al.  Forecasting Using Principal Components From a Large Number of Predictors , 2002 .

[21]  Gianni Amisano,et al.  Comparing Density Forecasts via Weighted Likelihood Ratio Tests , 2007 .

[22]  A. Atkeson,et al.  Are Phillips Curves Useful for Forecasting Inflation , 2001 .

[23]  Evolving Post-World War II U.K. Economic Performance , 2004 .

[24]  Giorgio E. Primiceri,et al.  Inflation-Gap Persistence in the U.S , 2008 .

[25]  C. De Mol,et al.  Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components? , 2006, SSRN Electronic Journal.

[26]  W. Newey,et al.  A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix , 1986 .

[27]  Argia M. Sbordone,et al.  Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve , 2008 .

[28]  E. George,et al.  Journal of the American Statistical Association is currently published by American Statistical Association. , 2007 .

[29]  C. Sims,et al.  Were There Regime Switches in U.S. Monetary Policy? , 2004 .

[30]  Lawrence J. Christiano,et al.  Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy , 2001, Journal of Political Economy.

[31]  T. Gneiting,et al.  Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules , 2011 .

[32]  T. Sargent,et al.  Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S. , 2003 .

[33]  Todd E. Clark,et al.  Forecasting with Small Macroeconomic VARs in the Presence of Instabilities , 2006 .

[34]  N. Shephard,et al.  Stochastic Volatility: Likelihood Inference And Comparison With Arch Models , 1996 .

[35]  Todd E. Clark.  Real-Time Density Forecasts from VARs with Stochastic Volatility , 2009 .

[36]  J. Stock,et al.  EFFICIENT TESTS FOR AN AUTOREGRESSIVE UNIT ROOT BY GRAHwA ELLIOrr, THOMAS , 2007 .

[37]  Min Wei,et al.  Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better? , 2005 .

[38]  J. Galí,et al.  Inflation Dynamics: A Structural Econometric Analysis , 1999 .

[39]  Todd E. Clark,et al.  Averaging Forecasts from Vars with Uncertain Instabilities , 2006 .

[40]  E. Fama,et al.  The Information in Long-Maturity Forward Rates , 1987 .

[41]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[42]  W. Wong,et al.  The calculation of posterior distributions by data augmentation , 1987 .

[43]  Arturo Estrella,et al.  The term structure as a predictor of real economic activity , 1991 .

[44]  Paul Newbold,et al.  Testing the equality of prediction mean squared errors , 1997 .

[45]  Jonathan H. Wright.  Forecasting U.S. Inflation by Bayesian Model Averaging , 2003 .

[46]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[47]  Argia M. Sbordone.  Prices and unit labor costs: a new test of price stickiness $ , 2002 .

[48]  R. Kohn,et al.  Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models , 2005 .

[49]  A. Haldane,et al.  UK Phillips curves and monetary policy , 1999 .

[50]  R. Kohn,et al.  Efficient Bayesian Inference for Dynamic Mixture Models , 2000 .

[51]  P. Giordani,et al.  Forecasting Macroeconomic Time Series with Locally Adaptive Signal Extraction , 2009 .

[52]  Giorgio E. Primiceri.  Time Varying Structural Vector Autoregressions and Monetary Policy , 2002 .

[53]  T. Sargent,et al.  Evolving Post-World War II U.S. Inflation Dynamics , 2001, NBER Macroeconomics Annual.

[54]  W. Newey,et al.  A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix , 1986 .

[55]  Glenn D. Rudebusch,et al.  The Macroeconomy and the Yield Curve: A Dynamic Latent Factor Approach , 2004 .

[56]  Simon M. Potter,et al.  Estimation and forecasting in models with multiple breaks , 2007 .

[57]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[58]  J. Stock,et al.  Modeling Inflation after the Crisis , 2010 .

[59]  Davide Pettenuzzo,et al.  Forecasting Time Series Subject to Multiple Structural Breaks , 2004, SSRN Electronic Journal.

[60]  Andrew T. Levin,et al.  Is Inflation Persistence Intrinsic in Industrial Economies? , 2003, SSRN Electronic Journal.

[61]  Dick van Dijk,et al.  Testing for Volatility Changes in U.S. Macroeconomic Time Series , 2004, Review of Economics and Statistics.

[62]  Todd E. Clark,et al.  Advances in Forecast Evaluation , 2011 .