Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS
Lili Ding and
Applied Energy, 2018, vol. 216, issue C, 132-141
This paper presents a real-time forecasting procedure that utilizes multiple factors with different sampling frequencies to predict the weekly carbon price. Novel combination-MIDAS models with five weight-type schemes are proposed for evaluating the forecast accuracy. The evidence shows that combination-MIDAS models provide forecasting performance gains over traditional models, which supports the use of mixed-frequency data that consist of economic and energy indicators to forecast the weekly carbon price. It is also shown that, Coal is the best predictor for carbon price forecasting and that forecasts that are based on Crude have similar trends to actual carbon prices but are higher than the actual prices.
Keywords: Carbon price; MIDAS regression; Forecast combination (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:216:y:2018:i:c:p:132-141
Ordering information: This journal article can be ordered from
http://www.elsevier. ... 405891/bibliographic
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Haili He ().