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

Heather L. R. Tierney ()
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Heather L. R. Tierney: Purdue University Fort Wayne

International Advances in Economic Research, 2019, vol. 25, issue 1, 39-63

Abstract: Abstract 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.

Keywords: Inflation persistence; Real-time data; Monetary policy; Nonparametrics; Forecasting (search for similar items in EconPapers)
JEL-codes: E52 C14 C53 (search for similar items in EconPapers)
Date: 2019
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