Estimating Internet Users' Demand Characteristics

Mingzhi Li (), Alok Gupta (), Boris Jukic (), Dale Stahl and Andrew B. Whinston ()
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Mingzhi Li: University of Texas at Austin
Alok Gupta: University of Connecticut
Boris Jukic: George Mason University
Andrew B. Whinston: University of Texas at Austin

No 523, Computing in Economics and Finance 1999 from Society for Computational Economics

Abstract: In this paper we investigate two parametric approaches and one non-parametric approach to estimating Internet users' value of time, an important characteristic of demand for Internet services. The advantages of these approaches are brought out, and the limitations are discussed. The models are tested using data generated from our simulation model of the Internet economy. First, we set up parametric count-data models given the characteristics of the data. While reasonably good results are obtained on all medium and large sized jobs, the method fails on small sized jobs. Second, we develop a nonparametric quasi-Bayesian update algorithm of retrieving the underlying distribution function of Internet users' value of time purely from observations of their choices. Compared with the parametric count data models, good results are obtained on all jobs.

Date: 1999-03-01
New Economics Papers: this item is included in nep-cmp and nep-ind
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Journal Article: Estimating Internet Users' Demand Characteristics (2001)
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