Using quantile regression to understand visitor spending

Ng, Pin T. and Lew, A. A. (2012) Using quantile regression to understand visitor spending. Journal of Travel Research, 51 (3). pp. 278-288. ISSN 0047-2875 (Submitted)

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Abstract

A common approach to assessing visitor expenditures is to use least-squares regression analysis to determine statistically significant variables upon which key market segments are identified for marketing purposes. This was done by Wang (2004) for survey data based on expenditures by Mainland Chinese visitors to Hong Kong. In this research note we use this same dataset to demonstrate the benefits of using quantile regression analysis to better identify tourist spending patterns and market segments. The quantile regression method measures tourist spending in different categories against a fixed range of dependent variables, which distinguishes between lower, medium, and higher spenders. The results show that quantile regression is less susceptible to influence by outlier values and is better able to target finer tourist spending market segments.

Item Type:	Article
ID number or DOI:	10.1177/0047287511410319
Keywords:	quantile regression, least-squared regression, Hong Kong, tourist expenditures, Chinese tourists, market segmentation
Subjects:	G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography
NAU Depositing Author Academic Status:	Faculty/Staff
Department/Unit:	The W.A. Franke College of Business College of Social and Behavioral Science > Geography, Planning and Recreation
Date Deposited:	06 Jan 2016 18:09
URI:	http://openknowledge.nau.edu/id/eprint/2275

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