Asymmetric loss function in product-level sales forecasting: An empirical comparison

Stepan Gogolev () and Evgeniy Ozhegov ()
Additional contact information
Stepan Gogolev: HSE university, Perm, Russsia
Evgeniy Ozhegov: HSE university, Saint Petersburg, Russia

Applied Econometrics, 2023, vol. 70, 109-121

Abstract: In the paper we study the behavior of models estimated using asymmetric loss function for the prediction of product-level sales. The paper is focused on the deriving of a loss function from the newsvendor model where the cost of sales over- and underprediction are not equal. We describe the properties of the asymmetric loss function and validate its performance on transactional sales data. The results show that when costs of sales over- and underprediction are non-equal, the prediction function obtained using asymmetric loss leads to lower economic costs compared with symmetric one. Our findings suggest implementing this type of forecasting method to predict product-level sales in the retail and restaurant industries to better accommodate business goals when solving inventory planning tasks.

Keywords: demand estimation; loss function; accuracy metric; prediction; retail (search for similar items in EconPapers)
JEL-codes: C52 C53 D12 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://pe.cemi.rssi.ru/pe_2023_70_109-121.pdf Full text (application/pdf)

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:ris:apltrx:0473

Access Statistics for this article

Applied Econometrics is currently edited by Anatoly Peresetsky

More articles in Applied Econometrics from Russian Presidential Academy of National Economy and Public Administration (RANEPA)
Bibliographic data for series maintained by Anatoly Peresetsky ().