Sparse Regularization in Marketing and Economics

29 Pages Posted: 20 Aug 2017 Last revised: 10 Feb 2018

See all articles by Guanhao Feng

Guanhao Feng

City University of Hong Kong (CityUHK)

Nick Polson

University of Chicago - Booth School of Business

Yuexi Wang

University of Chicago, Students

Jianeng Xu

University of Chicago, Students

Date Written: February 6, 2018

Abstract

Sparse alpha-norm regularization has many data-rich applications in Marketing and Economics. Alpha-norm, in contrast to lasso and ridge regularization, jumps to a sparse solution. This feature is attractive for ultra high-dimensional problems that occur in demand estimation and forecasting. The alpha-norm objective is nonconvex and requires coordinate descent and proximal operators to find the sparse solution. We study a typical marketing demand forecasting problem, grocery store sales for salty snacks, that has many dummy variables as controls. The key predictors of demand include price, equivalized volume, promotion, flavor, scent, and brand effects. By comparing with many commonly used machine learning methods, alpha-norm regularization achieves its goal of providing accurate out-of-sample estimates for the promotion lift effects. Finally, we conclude with directions for future research.

Keywords: Machine learning, Regularization, Proximal Algorithm, Nonconvex Optimization, Marketing Demand Forecasting

JEL Classification: C20, C52, C55, D12

Suggested Citation

Feng, Guanhao and Polson, Nick and Wang, Yuexi and Xu, Jianeng, Sparse Regularization in Marketing and Economics (February 6, 2018). Available at SSRN: https://ssrn.com/abstract=3022856 or http://dx.doi.org/10.2139/ssrn.3022856

Guanhao Feng (Contact Author)

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon Tong
Hong Kong

Nick Polson

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-7513 (Phone)
773-702-0458 (Fax)

Yuexi Wang

University of Chicago, Students ( email )

Chicago, IL
United States

Jianeng Xu

University of Chicago, Students ( email )

Chicago, IL
United States

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