Title: An empirical test of Tobit model robustness in estimating online auction prices over various distributions

Authors: Ming Zhou; Shaonan Tian; Taeho Park

Addresses: School of Global Leadership and Innovation, Lucas College and Graduate School of Business, San Jose State University, CA, 95192, USA ' Department of Marketing and Decision Sciences, Lucas College and Graduate School of Business, San Jose State University, CA, 95192, USA ' School of Global Leadership and Innovation, Lucas College and Graduate School of Business, San Jose State University, CA, 95192, USA

Abstract: Data censoring is a common issue in estimating demand and pricing data. The issue is often handled by Tobit models with normal distribution being assumed for its maximum likelihood function. Realistically, datasets can deviate from normal distributions. In this research, we specifically tested Tobit model robustness under distribution variations in online auction markets. We collected data from online auction markets and tested Tobit model robustness against various distributions. Our conclusion showed that Tobit model turned out to be fairly robust. This research provided empirical evidences for the robustness of Tobit estimations in online auction markets.

Keywords: Tobit model; robustness; online auction; pricing.

DOI: 10.1504/IJMOR.2017.084160

International Journal of Mathematics in Operational Research, 2017 Vol.10 No.4, pp.450 - 461

Received: 13 May 2015
Accepted: 25 Jul 2015

Published online: 16 May 2017 *

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