Oscar Claveria, Enric Monte and Salvador Torra
This study aims to apply a new forecasting approach to improve predictions in the hospitality industry. To do so, the authors developed a multivariate setting that allows the…
Abstract
Purpose
This study aims to apply a new forecasting approach to improve predictions in the hospitality industry. To do so, the authors developed a multivariate setting that allows the incorporation of the cross-correlations in the evolution of tourist arrivals from visitor markets to a specific destination in neural network models.
Design/methodology/approach
This multiple-input-multiple-output approach allows the generation of predictions for all visitor markets simultaneously. Official data of tourist arrivals to Catalonia (Spain) from 2001 to 2012 were used to generate forecasts for one, three and six months ahead with three different networks.
Findings
The study revealed that multivariate architectures that take into account the connections between different markets may improve the predictive performance of neural networks. Additionally, the authors developed a new forecasting accuracy measure and found that radial basis function networks outperform the rest of the models.
Research limitations/implications
This research contributes to the hospitality literature by developing an innovative framework to improve the forecasting performance of artificial intelligence techniques and by providing a new forecasting accuracy measure.
Practical implications
The proposed forecasting approach may prove very useful for planning purposes, helping managers to anticipate the evolution of variables related to the daily activity of the industry.
Originality/value
A multivariate neural network framework has been developed to improve forecasting accuracy, providing professionals with an innovative and practical forecasting approach.
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Keywords
Ongo Nkoa Bruno Emmanuel, Dobdinga Cletus Fonchamnyo, Mamadou Asngar Thierry and Gildas Dohba Dinga
The continuous increase in the negative gap between biocapacity and ecological footprint has remained globally persistent since early 1970. The purpose of this study is to examine…
Abstract
Purpose
The continuous increase in the negative gap between biocapacity and ecological footprint has remained globally persistent since early 1970. The purpose of this study is to examine the effect of foreign capital, domestic capital formation, institutional quality and democracy on ecological footprint within a global panel of 101 countries from 1995 to 2017.
Design/methodology/approach
The empirical procedure is based on data mix. To this end, this study uses a battery of testing and estimation approaches both conventional (no cross-sectional dependence [CD]) and novel approaches (accounting for CD). Among the battery of estimation techniques used, there are the dynamic ordinary least square, the mean group, the common correlation effect mean group technique, the augmented mean group technique, the Pooled mean group and the dynamic common correlation effect technique with the desire to obtain outcomes robust to heteroskedasticity, endogeneity, cross-correlation and CD among others.
Findings
The estimated outcomes indicate that using different estimators’ domestic capital formation consistently degrades the environment through an increase in ecological footprint, while institutional quality consistently enhances the quality of the environment. Further, the outcome reveals that, though foreign capital inflow degrades the environment, the time period is essential, as it shows a short-run environmental improvement and a long-run environmental degradation. Democratic activities show a mixed outcome with short-run degrading effect and a long-run enhancement effect on environmental quality.
Practical implications
Green investment should be the policy target of all economies, and these policies should be adopted to target both domestic capital and foreign capital alike. Second, the adoption of democratic practices will produce good leaders that will not just design short-term policies to blindfold the populace temporary but those that will produce long-term-oriented practices that will better and enhance the quality of the environment through the reduction of the global footprint. Equally, enhancing the institutional framework like respect for the rule of law in matters of abatement should be encouraged.
Originality/value
Although much research on the role of macroeconomic indicators on environmental quality has been done this far, democratic practices, intuitional quality and domestic capital have been given little attention. This research fills this gap by considering robust empirical techniques.
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This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016.
Abstract
Purpose
This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016.
Design/methodology/approach
For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe.
Findings
The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques.
Originality/value
The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps.