Crude Oil Spot Price Forecasting Using Ivanov-Based LASSO Vector Autoregression

Yishan Ding, Dongwei He, Jun Wu, Xiang Xu and Fanlin Meng

Complexity, 2022, vol. 2022, 1-10

Abstract: This paper proposes a forecasting methodology that investigates a set of different sparse structures for the vector autoregression (VAR) model using the Ivanov-based least absolute shrinkage and selection operator (LASSO) framework. The variant auxiliary problem principle method is used to solve the various Ivanov-based LASSO-VAR variants, which is supported by parallel computing with simple closed-form iteration and linear convergence rate. A test case with ten crude oil spot prices is used to demonstrate the improvement in forecasting skills gained from exploring sparse structures. The proposed method outperformed the conventional vector autoregressive model.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2022/5011174.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2022/5011174.xml (application/xml)

Related works:
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:hin:complx:5011174

DOI: 10.1155/2022/5011174

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().