Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy

Ellis W. Tallman, Saeed Zaman

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
7 Downloads (Pure)


This paper constructs hybrid forecasts that combine forecasts from vector autoregressive (VAR) model(s) with both short- and long-term expectations from surveys. Specifically, we use the relative entropy to tilt one-step-ahead and long-horizon VAR forecasts to match the nowcasts and long-horizon forecasts from the Survey of Professional Forecasters. We consider a variety of VAR models, ranging from simple fixed-parameter to time-varying parameters. The results across models indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. Accuracy improvements are achieved for a range of variables, including those that are not tilted directly but are affected through spillover effects from tilted variables. The accuracy gains for hybrid inflation forecasts from simple VARs are substantial, statistically significant, and competitive to time-varying VARs, univariate benchmarks, and survey forecasts. We view our proposal as an indirect approach to accommodating structural change and moving end points.

Original languageEnglish
Pages (from-to)373-398
Number of pages26
JournalInternational Journal of Forecasting
Issue number2
Early online date5 Oct 2019
Publication statusPublished - 30 Apr 2020


  • relative entropy
  • survey forecasts
  • structural change
  • density forecasts


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