Forecasting inflation using univariate continuous-time stochastic models

Kevin Fergusson*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

Abstract

In this paper we investigate the applicability of several continuous-time stochastic models to forecasting inflation rates with horizons out to 20 years. While the models are well known, new methods of parameter estimation and forecasts are supplied, leading to rigorous testing of out-of-sample inflation forecasting at short and long time horizons. Using US consumer price index data we find that over longer forecasting horizons—that is, those beyond 5 years—the log-normal index model having Ornstein–Uhlenbeck drift rate provides the best forecasts.

Original languageEnglish
Pages (from-to)37-46
Number of pages10
JournalJournal of Forecasting
Volume39
Issue number1
Early online date16 Dec 2019
DOIs
Publication statusPublished - Jan 2020
Externally publishedYes

Fingerprint Dive into the research topics of 'Forecasting inflation using univariate continuous-time stochastic models'. Together they form a unique fingerprint.

Cite this