On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation

Mauro Costantini, Robert Kunst

Publications: Contribution to journalArticlePeer Reviewed


To select a forecast model among competing models, researchers often use ex-ante prediction experiments over training samples. Following Diebold and Mariano (1995), forecasters routinely evaluate the relative performance of competing models with accuracy tests and may base their selection on test significance on top of comparing forecast errors. With extensive Monte Carlo analysis, we investigated whether this practice favors simpler models over more complex ones, without gains in forecast accuracy. We simulated the autoregressive moving-average model, the self-exciting threshold autoregressive model, and vector autoregression. We considered two variants of the Diebold–Mariano test, the test by Giacomini and White (2006), the -test by Clark and McCracken (2001), the Akaike information criterion, and a pure training-sample evaluation. The findings showed some accuracy gains for small samples when applying accuracy tests, particularly for the Clark–McCracken and bootstrapped Diebold–Mariano tests. Evidence against this testing procedure dominated, however, and training-sample evaluations without accuracy tests performed best in many cases.
Original languageEnglish
Pages (from-to)445-460
Number of pages16
JournalInternational Journal of Forecasting
Issue number2
Early online dateAug 2020
Publication statusPublished - 2021

Austrian Fields of Science 2012

  • 502025 Econometrics


  • forecasting
  • time series
  • predictive accuracy
  • Model selection
  • Time series
  • Predictive accuracy
  • Forecasting
  • Monte Carlo simulation

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