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En este trabajo presentamos un nuevo procedimiento para seleccionar modelos econométricos. Está basado en un enfoque heurístico, empleando algoritmos genéticos, que permite explorar el universo de modelos disponibles a partir de un modelo general sin restricciones. Este proceso de búsqueda del modelo óptimo está guiado únicamente por el criterio de información de Schwarz, que actúa como la función pérdida de un algoritmo genético empleado para seleccionar el modelo óptimo. Este procedimiento muestra buen comportamiento en relación a otras metodologías alternativas. A modo de ejemplos de su utilidad se muestran tres problemas donde el algoritmo ha sido empleado con éxito: la selección de variables que explican el crecimiento económico, la predicción del fracaso empresarial y la formación de una cartera de pocos activos que siga el comportamiento de un índice bursátil como el IBEX35 Español. ABSTRACT In this paper we present a new procedure for selecting econometric models. It is based on a heuristic approach, called genetic algorithms, which permits us to explore the universe of available models starting from a general model without restrictions. This search process for the optimal model is guided only by the Schwarz Information Criterion, which acts as the lost function of the genetic algorithm employed for selecting the optimum. This procedure shows good performance with respect to other methodologies. As examples of its utility three problems where the algorithm was successfully employed are presented: the selection of variables that explain economic growth, the prediction of failure of firms and the construction of a portfolio with few actives that follows the behaviour of a stock exchange index such as the Spanish IBEX35.

The aim of this study is to determine the presence or otherwise of transient and/or persistent cost inefficiency in the lodging industry. To do so, we applied the method proposed by Filippini and Greene, within a stochastic frontier panel data framework. Our empirical analysis is based on data for the hotel industry in the Canary Islands (Spain), an important destination for European tourists, for the period 2002-2015. Using a stochastic translog cost frontier model, we show that the industry's real output during the study period could have been achieved at 17% less cost if persistent (systematic) inefficiency were eliminated, and at 24% less cost in the absence of transient (nonsystematic) inefficiency. We also present evidence of factors that strongly influence hotels' transient cost efficiency, such as the positive effects of market share (in total revenue terms) and certain management characteristics, including the degree of independence with respect to shareholders, the experience in the industry (years in business) and the number of subsidiaries. [ABSTRACT FROM AUTHOR]

This study was conducted to analyse the influence of technological differences on hotel efficiency in the Canary Islands (Spain), with particular regard to the heterogeneity observed in hotel ownership and size. A metafrontier approach, based on non-parametric deterministic efficiency methods (data envelopment analysis and free-disposal hull) and robust non-parametric estimators (order-α), is used. This empirical analysis considered a panel data sample selection model of Canary Islands hotels for the period 2002–2015. The results obtained show that the frontiers against which the hotels are compared (metafrontier or group) and the consideration or otherwise of outliers are factors of crucial importance. We find that efficiency depends on hotel size (large hotels are more efficient than small ones), but not on the type of ownership. The results also show that the impact of the global financial crisis on the average technical efficiency of these hotels was slight or non-existent. Finally, the technological gap narrowed over time, especially in large hotels and those with no majority shareholder. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new methology for Index Tracking (IT) by means of cointegration which provides some significant improvements on that field. As the quality of the tracking portfolio (TP) depends highly on the stock selection procedure, we propose picking the stocks using a model selection technique based on optimizing the cointegration level of the TP and the benchmark index instead of selecting, as in previous papers the assets by ad hoc decisions. To illustrate an empirical application of these techniques we use daily closing prices in the Dow Jones Industrial Average (DJIA) index over two different periods; one period which goes from 1 January 1990 to 31 December 2001 previously used by other authors, and the bear and a turmoil period, which goes from January 2007 to May 2012, inside the current financial crisis. Using only five assets we are able to successfully track the DJIA index and our results improve the IT technique based on cointegration that chooses stocks with maximum capitalization level. We also have compared our results with a more traditional procedure based on correlation and again our results reveal superiority. The empirical illustration not only has been focused on the TP itself, but has also been extended to tracking the index with an added profitability of 5, 10, 15 or 20% and to long-short strategies, producing profitable results. [ABSTRACT FROM AUTHOR]