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

Mauro Costantini and Robert Kunst ()

International Journal of Forecasting, 2021, vol. 37, issue 2, 445-460

Abstract: 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.

Keywords: Forecasting; Time series; Predictive accuracy; Model selection; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207020301011
Full text for ScienceDirect subscribers only

Related works:
Working Paper: On Using Predictive-ability Tests in the Selection of Time-series Prediction Models: A Monte Carlo Evaluation (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:2:p:445-460

DOI: 10.1016/j.ijforecast.2020.06.010

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().