Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Hospitality Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Hospitality Management, 72, 2018 DOI: 10.1016/j.ijhm.2018.01.009
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Changing The Basics
T2 - Toward More Use of Quantile Regressions in Hospitality and Tourism Research
AU - Assaf, A. George
AU - Tsionas, Mike
N1 - This is the author’s version of a work that was accepted for publication in International Journal of Hospitality Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Hospitality Management, 72, 2018 DOI: 10.1016/j.ijhm.2018.01.009
PY - 2018/6
Y1 - 2018/6
N2 - The aim of this paper is to encourage more use of Quantile Regressions (QRs) in hospitality and tourism research. More importantly, we focus on the Bayesian estimation of QRs and discuss its advantages over traditional estimation techniques. We also discuss a Bayesian QR model that accounts for heteroscedasticity. We illustrate the performance of the two models using an interesting application on corporate social responsibility and firm value.
AB - The aim of this paper is to encourage more use of Quantile Regressions (QRs) in hospitality and tourism research. More importantly, we focus on the Bayesian estimation of QRs and discuss its advantages over traditional estimation techniques. We also discuss a Bayesian QR model that accounts for heteroscedasticity. We illustrate the performance of the two models using an interesting application on corporate social responsibility and firm value.
KW - Quantile Regressions
KW - Heteroscedasticity
KW - Bayesian Estimation
U2 - 10.1016/j.ijhm.2018.01.009
DO - 10.1016/j.ijhm.2018.01.009
M3 - Journal article
VL - 72
SP - 140
EP - 144
JO - International Journal of Hospitality Management
JF - International Journal of Hospitality Management
SN - 0278-4319
ER -