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 language | English (US) |
|---|---|
| Pages (from-to) | 278-288 |
| Number of pages | 11 |
| Journal | Journal of Travel Research |
| Volume | 51 |
| Issue number | 3 |
| DOIs | |
| State | Published - May 2012 |
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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 journal › Article
}
TY - JOUR
T1 - Using quantile regression to understand visitor spending
AU - Lew, Alan A
AU - Ng, Pin T
PY - 2012/5
Y1 - 2012/5
N2 - 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.
AB - 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.
KW - Chinese tourists
KW - Hong Kong
KW - least squares regression
KW - market segmentation
KW - quantile regression
KW - tourist expenditures
UR - http://www.scopus.com/inward/record.url?scp=84859053678&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84859053678&partnerID=8YFLogxK
U2 - 10.1177/0047287511410319
DO - 10.1177/0047287511410319
M3 - Article
AN - SCOPUS:84859053678
VL - 51
SP - 278
EP - 288
JO - Journal of Travel Research
JF - Journal of Travel Research
SN - 0047-2875
IS - 3
ER -