Machine learning applications in household-level demand prediction

Shuoli Zhao, Yufeng Lai, Chenglong Ye and Keehyun Lee

Applied Economics Letters, 2024, vol. 31, issue 1, 5-11

Abstract: Machine learning (ML) is becoming one of the most anticipated methods in predicting consumer demand. However, it is still uncertain how ML methods perform relative to traditional econometric methods under different dataset scales. This study estimates and compares the out-of-sample predictive accuracy of household budget share for organic fresh produce using two parametric models and six ML methods under regular and large sample sizes. Results show that ML method, particularly Logistic Elastic Net, performs better than econometric models under regular sample size. Contrarily, when dealing with big data, econometric models reach to same accuracy level as ML methods whereas random forest presents a possible overfitting problem. This study illustrates the competence of ML methods in demand prediction, but choosing the optimal method needs to consider product specifics, sample sizes, and observable features.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2022.2120954 (text/html)
Access to full text is restricted to subscribers.

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:taf:apeclt:v:31:y:2024:i:1:p:5-11

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2022.2120954

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().