Machine Learning Methods for Demand Estimation

Autor:	Denis Nekipelov, Patrick Bajari, Stephen P. Ryan, Miaoyu Yang
Rok vydání:	2015
Předmět:	Estimation Economics and Econometrics jel:C52 business.industry jel:C20 Model selection jel:D83 Demand estimation jel:D12 Scanner panel data Machine learning computer.software_genre Set (abstract data type) Linear regression Economics Artificial intelligence business computer
Zdroj:	American Economic Review. 105:481-485
ISSN:	0002-8282
DOI:	10.1257/aer.p20151021
Popis:	We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e414163af10cb340b4109a1c7eec7db https://doi.org/10.1257/aer.p20151021 Zobrazit plný text záznamu Plný text ve formátu PDF