Demand estimation with infrequent purchases and small market sizes

Author:

Hortaçsu Ali12,Natan Olivia R.3,Parsley Hayden4,Schwieg Timothy5,Williams Kevin R.62

Affiliation:

1. Department of Economics, University of Chicago

2. NBER

3. Haas School of Business, University of California

4. Department of Economics, University of Texas

5. Booth School of Business, University of Chicago

6. School of Management, Yale University

Abstract

We propose a demand estimation method that allows for a large number of zero‐ sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market‐level data as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand.

Funder

National Bureau of Economic Research

Publisher

The Econometric Society

Subject

Economics and Econometrics

Reference44 articles.

1. Abaluck, Jason, Giovanni Compiani, and Fan Zhang (2022), “A method to estimate discrete choice models that is robust to consumer search.” Working Paper.

2. Demand Estimation Under the Multinomial Logit Model from Sales Transaction Data

3. Adam, Hammaad, Pu He, and Fanyin Zheng (2020), “Machine learning for demand estimation in long tail markets.” Working Paper.

4. Amano, Tomomichi, Andrew Rhodes, and Stephan Seiler (2022), “Flexible demand estimation with search data.” Working Paper.

5. Armona, Luis, Greg Lewis, and Georgios Zervas (2021), “Learning product characteristics and consumer preferences from search data.” Working Paper.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3