Forecasting with the Nonparametric Exclusion-from-Core Inflation Persistence Model Using Real-Time Data

  • Heather L. R. TierneyEmail author


This paper contributes to nonparametric forecasting techniques by developing three local nonparametric forecasting methods for the nonparametric exclusion-from-core inflation persistence model that are capable of utilizing revised real-time personal consumption expenditure and core personal consumption expenditure for 62 vintages. Local nonparametric forecasting provides forecasters with a way of parsing the data by permitting a low inflation measure to be included in other low inflationary time periods and vice versa. Furthermore, when examining real-time data, policy-makers can use the nonparametric models to help identify outliers and potential abnormal economic events and problems with the data such as an underlying change in volatility. The most efficient nonparametric forecasting method is the third model, which uses the flexibility of nonparametrics by making forecasts conditional on the forecasted value, which can be used for counterfactual analysis.


Inflation persistence Real-time data Monetary policy Nonparametrics Forecasting 

JEL Classification

E52 C14 C53 



I would like to thank in alphabetical order the following people for their gracious comments: Marcelle Chauvet, Graham Elliott, James Hamilton, Hedayeh Samavati, Andres Santos, Zeynep Senyuz, Jack Strauss, Allan Timmermann, and Emre Yoldas, and last but not least, the participants of the 19th Annual Symposium of the Society for Nonlinear Dynamics and Econometrics (2011), the Southern Economic Association (SEA) Meeting 2011, Lafayette College Economics Seminar Series, and the University of California San Diego (UCSD) Econometrics Seminar Series (2013). I also give a very special thanks to Dean Croushore for graciously sharing his knowledge of real-time data with me.

Supplementary material

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ESM 1 (DOCX 123 kb)


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Copyright information

© International Atlantic Economic Society 2019

Authors and Affiliations

  1. 1.Economics Department, Doermer School of BusinessPurdue University Fort WayneFort WayneUSA

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