Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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 F-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.