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决策的熵方法:旅游需求中的不确定性周期

Entropy Method for Decision-Making: Uncertainty Cycles in Tourism Demand.

作者信息

Ruiz Reina Miguel Ángel

机构信息

Department of Theory and Economic History, PhD Program in Economic and Business of University of Malaga, Plaza del Ejido, s/n, 29013 Malaga, Spain.

出版信息

Entropy (Basel). 2021 Oct 20;23(11):1370. doi: 10.3390/e23111370.

DOI:10.3390/e23111370
PMID:34828069
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8623377/
Abstract

A new methodology is presented for measuring, classifying and predicting the cycles of uncertainty that occur in temporary decision-making in the tourist accommodation market (apartments and hotels). Special attention is paid to the role of entropy and cycles in the process under the Adaptive Markets Hypothesis. The work scheme analyses random cycles from time to time, and in the frequency domain, the linear and nonlinear causality relationships between variables are studied. The period analysed is from January 2005 to December 2018; the following empirical results stand out: (1) On longer scales, the periodicity of the uncertainty of decision-making is between 6 and 12 months, respectively, for all the nationalities described. (2) The elasticity of demand for tourist apartments is approximately 1% due to changes in demand for tourist hotels. (3) The elasticity of the uncertainty factor is highly correlated with the country of origin of tourists visiting Spain. For example, it has been empirically shown that increases of 1% in uncertainty cause increases in the demand for apartments of 2.12% (worldwide), 3.05% (UK), 1.91% (Germany), 1.78% (France), 7.21% (Ireland), 3.61% (The Netherlands) respectively. This modelling has an explanatory capacity of 99% in all the models analysed.

摘要

本文提出了一种新的方法,用于测量、分类和预测旅游住宿市场(公寓和酒店)临时决策中出现的不确定性周期。特别关注了适应性市场假说下熵和周期在该过程中的作用。工作方案不时分析随机周期,并在频域中研究变量之间的线性和非线性因果关系。分析的时间段为2005年1月至2018年12月;以下实证结果较为突出:(1)在较长尺度上,所有所述国籍的决策不确定性周期分别在6至12个月之间。(2)由于旅游酒店需求的变化,旅游公寓需求的弹性约为1%。(3)不确定性因素的弹性与访问西班牙的游客来源国高度相关。例如,经验表明,不确定性增加1%会导致公寓需求分别增加2.12%(全球)、3.05%(英国)、1.91%(德国)、1.78%(法国)、7.21%(爱尔兰)、3.61%(荷兰)。在所有分析的模型中,这种建模的解释能力为99%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a10/8623377/c5138435ab48/entropy-23-01370-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a10/8623377/a2a8beea8137/entropy-23-01370-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a10/8623377/9d53ee69a9ed/entropy-23-01370-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a10/8623377/6a9325232ade/entropy-23-01370-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a10/8623377/c5138435ab48/entropy-23-01370-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a10/8623377/a2a8beea8137/entropy-23-01370-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a10/8623377/9d53ee69a9ed/entropy-23-01370-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a10/8623377/6a9325232ade/entropy-23-01370-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a10/8623377/c5138435ab48/entropy-23-01370-g004a.jpg

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