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Abstract

Cycles and Uncertainty: Applications in the Tourist Accommodation Market †

Department of Theory and Economic History (Staff of Fundamentals), PhD Program in Economics and Business, s/n, University of Málaga, Plaza del Ejido, 29013 Málaga, Spain
Presented at the 7th International Conference on Time Series and Forecasting, Gran Canaria, Spain, 19–21 July 2021.
Eng. Proc. 2021, 5(1), 3; https://doi.org/10.3390/engproc2021005003
Published: 24 June 2021
(This article belongs to the Proceedings of The 7th International conference on Time Series and Forecasting)

Abstract

:
In the socio-economic field, it is not surprising that decision-making is based on asymmetric information. Economic agents make decisions to forecast in primary and secondary industries related to the tourism sector. This study aims to provide knowledge in situations of asymmetric information with increasing randomness using time series for tourism accommodation markets. We are trying to solve the question of how consumers exchange their preferences for tourist accommodation between tourist apartments and hotel accommodation in Spain. The emergence of the sharing economy concept has emerged as a competitor to the traditional hotel accommodation in the tourist market. To do this, we will develop a theoretical framework to measure situations of uncertainty and their temporal evolution. Information Theory (IT) is the central axis of the study, particularly the concept of entropy. The Shannon entropy (SE) concept is a static measure of information. This work proposes to model the temporal arrangement of SE to discover the behaviors of the systems. The study in the domain of time and frequency allows us to understand the cycles of uncertainty between systems. To apply the theoretical framework, we will work with data from official Spanish sources for tourist accommodation from January 2008 to December 2019. The results of the empirical analysis show the decision changes of economic agents according to a seasonal pattern. Consumers have new accommodation options, and the answer we get from this work is that consumers have different preferences depending on seasonality. The use of SE allows us to make better predictions compared to SARIMA models, the traditional modelling of seasonal dummy variables, and VAR models. The results of the Matrix U1 Theil verify this hypothesis. The theoretical framework and empirical analysis find an answer to asymmetric information. The implications of this work contribute to the field of social sciences related to the tourism sector, in particular to thermodynamics, statistical mechanics, and IT. The modelling of uncertainty allows for the forecasting and control of accommodation tourist markets in random situations. The applications of this study can be tested in other areas of the economy such as finance, transportation, or investment.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated during and analysed during the current study are available in the repository (https://drive.google.com/file/d/1TP63q-1IFW0_oRJHXBDBFJxMPANOTc9b/view?usp=sharing, accessed on 23 June 2021) [1,2,3,4,5,6,7].

Acknowledgments

The author wishes to acknowledge the support given by the University of Malaga. PhD. Program in Economics and Business, effective from 16 July 2013. Especially to Associate Professor Antonio Caparrós Ruiz from the University of Malaga for reviewing this work. Group of research: “SEJ157-INIDICADORES SOCIALES”.

Conflicts of Interest

The authors declare no conflict of interest.

References

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  2. Delgado-Bonal, A.; Marshak, A. Approximate entropy and sample entropy: A comprehensive tutorial. Entropy 2019, 541. [Google Scholar] [CrossRef] [PubMed][Green Version]
  3. Ruiz-Reina, M.Á. Entropy of Tourism: The unseen side of tourism accommodation. In Proceedings of the International Conference on Applied Research in Business, Management and Economics, Barcelona, Spain, 12–14 December 2019; Available online: https://www.dpublication.com/wp-content/uploads/2019/12/424.pdf (accessed on 23 June 2021).
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  6. Lütkepohl, H. New Introduction to Multiple Time Series Analysis; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2005. [Google Scholar]
  7. Reina, M.Á.R. Big Data: Forecasting and Control for Tourism Demand. In Theory and Applications of Time Series Analysis. ITISE 2019; Valenzuela, R.I.O., Rojas, F., Herrera, L.J., Pomares, H., Eds.; Springer: Cham, Switzerland, 2020; pp. 273–286. [Google Scholar]
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MDPI and ACS Style

Ruiz Reina, M.Á. Cycles and Uncertainty: Applications in the Tourist Accommodation Market. Eng. Proc. 2021, 5, 3. https://doi.org/10.3390/engproc2021005003

AMA Style

Ruiz Reina MÁ. Cycles and Uncertainty: Applications in the Tourist Accommodation Market. Engineering Proceedings. 2021; 5(1):3. https://doi.org/10.3390/engproc2021005003

Chicago/Turabian Style

Ruiz Reina, Miguel Ángel. 2021. "Cycles and Uncertainty: Applications in the Tourist Accommodation Market" Engineering Proceedings 5, no. 1: 3. https://doi.org/10.3390/engproc2021005003

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