.png){kind=icon}
Return
A Bayesian non-parametric stochastic frontier model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.annals.2020.103116.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we introduce a new Bayesian non-parametric stochastic frontier (SF) model that addresses the endogeneity problem and relaxes problematic assumptions regarding functional form, and distributional properties. The model can be seen as a competitor to DEA. We show how the model outperforms its parametric counterpart in all critical diagnostic tests. The application we use covers a unique sample of US hotels that operate within competitive clusters. We utilize the efficiency results obtained from this model to shed light on the extent to which performance spillover (i.e. agglomeration effects) may differ based on the varied characteristics of hotels within these clusters. We obtain interesting findings and discuss their implications for hotels contemplating future co-location strategies. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Non-parametric stochastic frontier
Bayesian
Minimal assumptions
US hotels
Competitive clusters
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.8
Papers:
4.5K
Citations:
1.8W
Organization
View more organizations
Cited Papers
ShareSaveShareSaveADAPTIVE BAYESIAN ESTIMATION USING A GAUSSIAN RANDOM FIELD WITH INVERSE GAMMA BANDWIDTH.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSaveShareSaveShareSaveEstimation of Profit Efficiency in the Hotel Industry Using a Bayesian Stochastic Frontier Model
PREAI
ShareSave
Tourism development and regional production efficiency: Evidence from southwestern ChinaTOURISM ECONOMICS
TOURISM ECONOMICSIF3.2
PREAI
ShareSave View more