Regression Tree Model for Analysis of Demand with Heterogeneity and Censorship

Evgeniy Ozhegov and Alina Ozhegova ()
Additional contact information
Alina Ozhegova: National Research University Higher School of Economics

HSE Working papers from National Research University Higher School of Economics

Abstract: In this research we analyze new approach for prediction of demand. In the studied market of performing arts the observed demand is limited by capacity of the house. Then one needs to account for demand censorhip to obtain unbiased estimates of demand funnction parameters. The presence of consumer segments with dierent purposes of going to the theatre and willingness-to-pay for performance and ticket characteristics causes a heterogeneity in theatre demand. We propose an estimator for prediction of demand that accounts for both demand censorhip and preferences heterogeneity. The estimator is based on the idea of classiffication and regression trees and bagging prediction aggregation extended for prediction of censored data. Our algorithm predicts and combines predictions for both discrete and continuous parts of censored data.We show that our estimator performs better in terms of prediction accuracy compared with estimators which accounts either for censorship, or heterogeneity only. The proposed approach is helpful for finding product segments and optimal price setting.

Keywords: demand; performing arts; machine learning; regression tree; censored data; pricing (search for similar items in EconPapers)
JEL-codes: C53 D12 Z11 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in WP BRP Series: Economics / EC, September 2017, pages 1-24

Downloads: (external link)
https://wp.hse.ru/data/2017/09/14/1173350151/174EC2017.pdf (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:hig:wpaper:174/ec/2017

Access Statistics for this paper

More papers in HSE Working papers from National Research University Higher School of Economics
Bibliographic data for series maintained by Shamil Abdulaev () and Shamil Abdulaev ().