Forecasting Inflation with a Random Walk

29 Pages Posted: 26 Dec 2012 Last revised: 17 Jul 2015

See all articles by Pablo M. Pincheira

Pablo M. Pincheira

Adolfo Ibanez University - School of Business

Carlos Medel

University of Nottingham

Date Written: December 26, 2012


The use of different time-series models to generate forecasts is fairly usual in the forecasting literature in general, and in the inflation forecast literature in particular. When the predicted variable is stationary, the use of processes with unit roots may seem counterintuitive. Nevertheless, in this paper we demonstrate that forecasting a stationary variable with driftless unit-root-based forecasts generates bounded Mean Squared Prediction Errors errors at every single horizon. We also show via simulations that persistent stationary processes may be better predicted by unit-root-based forecasts than by forecasts coming from a model that is correctly specified but that is subject to a higher degree of parameter uncertainty. Finally, we provide an empirical illustration in the context of CPI inflation forecasts for three industrialized countries.

Keywords: Inflation forecasts, unit root, univariate time-series models, out-of-sample comparison, random walk

JEL Classification: C22, C53, E31, E37

Suggested Citation

Pincheira, Pablo M. and Medel, Carlos, Forecasting Inflation with a Random Walk (December 26, 2012). Available at SSRN: or

Pablo M. Pincheira (Contact Author)

Adolfo Ibanez University - School of Business ( email )

Diagonal Las Torres 2640

Carlos Medel

University of Nottingham ( email )

University Park
Nottingham, NG8 1BB
United Kingdom

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