Using quantile regression to understand visitor spending

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

A common approach to assessing visitor expenditures is to use least squares regression analysis to determine statistically significant variables on which key market segments are identified for marketing purposes. This was earlier done by Wang for survey data based on expenditures by Mainland Chinese visitors to Hong Kong. In this research note, this same data set was used 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.

Original languageEnglish (US)
Pages (from-to)278-288
Number of pages11
JournalJournal of Travel Research
Volume51
Issue number3
DOIs
StatePublished - May 2012

Fingerprint

Regression analysis
tourist
regression
market
expenditure
regression analysis
expenditures
Marketing
outlier
marketing
Hong Kong
Market segments
Tourists
Quantile regression
Values
Expenditure
method
Regression method
Least squares
Survey data

Keywords

  • Chinese tourists
  • Hong Kong
  • least squares regression
  • market segmentation
  • quantile regression
  • tourist expenditures

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation

Cite this

Using quantile regression to understand visitor spending. / Lew, Alan A; Ng, Pin T.

In: Journal of Travel Research, Vol. 51, No. 3, 05.2012, p. 278-288.

Research output: Contribution to journalArticle

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