Forecasting with a random walk

Pablo M. Pincheira, Carlos A. Medel

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The use of different time-series models to generate forecasts is fairly usual in the fields of macroeconomics and financial economics. When the target 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 forecasts based on driftless unit-root processes generates bounded mean squared prediction errors at every single horizon. We also show that these forecasts are unbiased. In addition, we show via simulations that persistent stationary processes may be better predicted by driftless unit-rootbased forecasts than by forecasts coming from a model that is correctly specified but is subject to a higher degree of parameter uncertainty. Finally, we provide an empirical illustration of our findings in the context of CPI inflation forecasts for a sample of industrialized economies.

Original languageEnglish
Pages (from-to)539-564
Number of pages26
JournalFinance a Uver - Czech Journal of Economics and Finance
Issue number6
StatePublished - 2016
Externally publishedYes


  • Inflation forecasts
  • Out-of-sample comparison
  • Random walk
  • Unit root
  • Univariate time-series models


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