On Using Predictive-ability Tests in the Selection of Time-series Prediction Models: A Monte Carlo Evaluation

Costantini, Mauro and Kunst, Robert M. ORCID: https://orcid.org/0000-0001-6831-2471 (July 2018) On Using Predictive-ability Tests in the Selection of Time-series Prediction Models: A Monte Carlo Evaluation. Closed Series > Working Paper Series > IHS Economics Series 341, 38 p.

[img]
Preview
Text
es-341.pdf

Download (190kB) | Preview
[img] Text
user_agreement_es341.pdf
Restricted to Registered users only

Download (664kB) | Request a copy

Abstract or Table of Contents

Comparative ex-ante prediction experiments over expanding subsamples are a popular tool for the task of selecting the best forecasting model class in finite samples of practical relevance. Flanking such a horse race by predictive-accuracy tests, such as the test by Diebold and Mariano (1995), tends to increase support for the simpler structure. We are concerned with the question whether such simplicity boosting actually benefits predictive accuracy in finite samples. We consider two variants of the DM test, one with naive normal critical values and one with bootstrapped critical values, the predictive-ability test by Giacomini and White (2006), which continues to be valid in nested problems, the F test by Clark and McCracken (2001), and also model selection via the AIC as a benchmark strategy. Our Monte Carlo simulations focus on basic univariate time-series specifications, such as linear (ARMA) and nonlinear (SETAR) generating processes.

Item Type: IHS Series
Keywords: Forecasting, time series, predictive accuracy, model selection
Classification Codes (e.g. JEL): C22, C52, C53
Research Units: Macroeconomics and Economic Policy
Status: Published
Date Deposited: 08 Aug 2018 07:43
Last Modified: 27 Sep 2019 06:03
ISSN: 1605-7996
URI: https://irihs.ihs.ac.at/id/eprint/4712

Actions (login required)

View Item View Item