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Regression Tree Model for Analysis of Demand with Heterogeneity and Censorship

Author

Listed:
  • Evgeniy M. Ozhegov

    () (National Research University Higher School of Economics)

  • Alina Ozhegova

    () (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.

Suggested Citation

  • Evgeniy M. Ozhegov & Alina Ozhegova, 2017. "Regression Tree Model for Analysis of Demand with Heterogeneity and Censorship," HSE Working papers WP BRP 174/EC/2017, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:174/ec/2017
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    References listed on IDEAS

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    1. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    2. Henry Hansmann, 1981. "Nonprofit Enterprise in the Performing Arts," Bell Journal of Economics, The RAND Corporation, vol. 12(2), pages 341-361, Autumn.
    3. Jörg Schimmelpfennig, 1997. "Demand for Ballet: A Non-Parametric Analysis of the 1995 Royal Ballet Summer Season," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 21(2), pages 119-127, June.
    4. Throsby, David, 1994. "The Production and Consumption of the Arts: A View of Cultural Economics," Journal of Economic Literature, American Economic Association, vol. 32(1), pages 1-29, March.
    5. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74, pages 132-132.
    6. Gapinski, James H, 1984. "The Economics of Performing Shakespeare," American Economic Review, American Economic Association, vol. 74(3), pages 458-466, June.
    7. Abbe-Decarroux, Francois, 1994. "The perception of quality and the demand for services : Empirical application to the performing arts," Journal of Economic Behavior & Organization, Elsevier, vol. 23(1), pages 99-107, January.
    8. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Demand Estimation with Machine Learning and Model Combination," NBER Working Papers 20955, National Bureau of Economic Research, Inc.
    9. Matzkin, Rosa L., 2012. "Identification in nonparametric limited dependent variable models with simultaneity and unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 166(1), pages 106-115.
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    12. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
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    14. Seaman, Bruce A, 2006. "Empirical Studies of Demand for the Performing Arts," Handbook of the Economics of Art and Culture, Elsevier.
    15. Jonathan Corning & Armando Levy, 2002. "Demand for Live Theater with Market Segmentation and Seasonality," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 26(3), pages 217-235, August.
    16. Hong H. & Chernozhukov V., 2002. "Three-Step Censored Quantile Regression and Extramarital Affairs," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 872-882, September.
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    More about this item

    Keywords

    demand; performing arts; machine learning; regression tree; censored data; pricing;

    JEL classification:

    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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