Predicting Relative Forecasting Performance: An Empirical Investigation

43 Pages Posted: 8 Nov 2018 Last revised: 16 Nov 2018

See all articles by Eleonora Granziera

Eleonora Granziera

Bank of Finland

Tatevik Sekhposyan

Texas A&M University - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: November 8, 2018


The relative performance of forecasting models changes over time. This empirical observation raises two questions: is the relative performance itself predictable? If so, can it be exploited to improve forecast accuracy? We address these questions by evaluating the predictive ability of a wide range of economic variables for two key US macroeconomic aggregates, industrial production and inflation, relative to simple benchmarks. We find that business indicators, financial conditions, uncertainty as well as measures of past relative performance are generally useful for explaining the relative forecasting performance of the models. We further conduct a pseudo-real-time forecasting exercise, where we use the information about the conditional performance for model selection and model averaging. The newly proposed strategies deliver sizable improvements over competitive benchmark models and commonly used combination schemes. Gains are larger when model selection and averaging are based on financial conditions as well as past performance measured at the forecast origin date.

Keywords: Conditional Predictive Ability, Model Selection, Model Averaging, Inflation Forecasts, Output Growth Forecasts

JEL Classification: C22, C52, C53

Suggested Citation

Granziera, Eleonora and Sekhposyan, Tatevik, Predicting Relative Forecasting Performance: An Empirical Investigation (November 8, 2018). Bank of Finland Research Discussion Paper No. 23/2018, Available at SSRN:

Eleonora Granziera (Contact Author)

Bank of Finland ( email )

Helsinki, Helsinki 00100

HOME PAGE: http://

Tatevik Sekhposyan

Texas A&M University - Department of Economics ( email )

5201 University Blvd.
College Station, TX 77843-4228
United States

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