Forecasting Models Evaluation using A Slacks-based Context-dependent DEA Framework

Jamal Ouenniche, Xu Bing, Kaoru Tone

Research output: Contribution to journalArticlepeer-review

Abstract

Xu and Ouenniche (2012a) proposed an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) based model to address a common methodological issue in the evaluation of competing forecasting models; namely, ranking models based on a single performance measure at a time, which typically leads to conflicting ranks. However, their approach suffers from a number of issues. In this paper, we overcome these issues by proposing a slacks-based context-dependent DEA framework and use it to rank forecasting models of oil prices’ volatility.
Original languageEnglish
Pages (from-to)1477-1484
JournalJournal of Applied Business Research
Volume30
Issue number5
Publication statusPublished - 2014

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