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.
Highlights
This work aims to estimate the randomness of time series in data-generating processes without prior knowledge for decision-making
Agents must decide on two possible choices, thereby giving an idea of the objective pursued by our work, i.e., to determine the uncertainty cycles and the transmission of information between the two stochastic variables to be analysed
In order to do this, we assumed that the agents presented a function of constant utility and that prices were exogenous and budgetary restrictions existed
Summary
This work aims to estimate the randomness of time series in data-generating processes without prior knowledge for decision-making. To fully understand the process, it is necessary to develop a theoretical scheme to measure the uncertainty introduced in a nonlinear causal model. This allows us to identify the cycles of uncertainty. This measure of uncertainty provides unknown information in the data-generating process of the relationships between the variables to be analysed
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