预测碳价格:技术的作用是什么?

IF 2.7 3区 经济学 Q1 ECONOMICS
Ali Ben Mrad, Amine Lahiani, Salma Mefteh-Wali, Nada Mselmi
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引用次数: 0

摘要

我们使用大量的机器学习模型来检验该技术在预测碳价格方面的作用。预测者是从技术、环境、金融、能源和地缘政治方面挑选的。我们的样本涵盖了从2014年8月1日到2024年3月4日的日常时间。研究发现,技术因素(信息技术指数、AEX技术指数和科技全股指数)在单独和同时纳入预测模型时均显著提高了碳价格的预测精度。此外,Diebold-Mariano和Clark-West检验高度拒绝了技术模型和基线模型(不含技术变量)之间预测精度相等的零值。此外,结果表明,XGBoost在所有预测范围(1、5、22和250天)上都优于其他机器学习模型。我们提出了对投资者、公司和政策制定者有用的重要政策含义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Carbon Prices: What Is the Role of Technology?

We examine the role of the technology in predicting carbon prices using a large set of machine learning models. The predictors are selected from technological, environmental, financial, energy, and geopolitical aspects. Our sample covers the daily period from August 1, 2014, to March 4, 2024. We find that technology factors (Information Technology Index, AEX Technology Index, and Tech All Share Index) significantly improve the prediction accuracy of carbon prices, both when included in the prediction model individually and simultaneously. Furthermore, the Diebold–Mariano and Clark–West tests highly reject the null of equal predictive accuracy between the technology model and the baseline model (without technology variables). Moreover, results show that XGBoost outperforms the alternative machine learning models for all forecasting horizons (1, 5, 22, and 250 days). We present significant policy implications useful for investors, companies, and policymakers.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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