Entropy of Tourism: the unseen side of tourism accommodation

Abstract

The measurement of random situations is a relevant fact within the field of Econometrics. In the usual practice of the field of Econometrics assumptions are made about the statistical distributions of the data. In this paper, a concept from Physics is introduced, specifically the use of Entropy as an explanatory factor in the decision-making of tourist accommodation in apartments. The emergence of a concept called Sharing Economy has made the housing market to evolve. Assuming a direct competition against traditional accommodation such as hotels. For this, the Entropy time series have been calculated and defined for the periods between January 2005 and August 2018, to forecast and compare them with a time horizon of 12 months with the most usual predictive models. To carry out the forecast comparisons, we work with the new Matrix U1 Theil which allows quantifying the gain of the use of the Entropy models. The main locations in which the study has been applied are Spain, Catalonia and Barcelona. The theoretical implications and economic consequences are relevant for stakeholders in decisionmaking. The study details the relations of exchange in the decision making of accommodations in tourist apartments and details the relationship of exchange in situations of uncertainty with a high explanatory capacity of the models.

Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

“…This method of estimation, based on a matrix of instruments, allows obtaining consistency properties of the estimated parameters. The results of our forecasting model improve the data of models contrasted in the tourism forecasting literature such as the Entropy model [8], Seasonal Autoregressive Integrated Moving Average (SARIMA) [9] and Autoregressive Distributed Lags extended to Seasonality (ARDL + Seasonality) [10]. The results of Ratio Theil's (RT s U 1 ) verify these empirical results.…”

“…We propose the GMM + HAC-Newey-West matrix. Forecasting tasks will be compared with automatic TRAMO-SEATS for SARIMA models [26] and causality models such as Autoregressive Distributed Lags Extended to Seasonality, in addition to the causality model with Entropy factor [27]. For the evaluation of the prediction, we propose the Root Mean Squared Error (RMSE) criterion and the relative dimensionless criterion of RT s U 1 [10].…”

“…Of particular interest is the use of the Shannon Entropy dynamic to quantify the randomness in the decision between tourist accommodation in apartments and hotels. The authors highlight the descriptive and predictive goodness compared to the SARIMA predictive models, the measurement of the improvement in forecasting capacity is carried out with RT s U 1 [8]. This relative ratio can be classified in the measurements of goodness of the ex-post prediction by its interpretability [25].…”

“…This relative ratio can be classified in the measurements of goodness of the ex-post prediction by its interpretability [25]. It should be noted that previous studies of Entropy applied to tourism reveal a cyclical behaviour compatible with seasonal flows [8].…”

“…The results of Ratio Theil's (RT s U 1 ) verify these empirical results. In this paper, we work with models with Seasonality mainly because previous studies of uncertainty analysis have demonstrated their existence [8].…”