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A list of new tests for serial dependence based on ordinal patterns is provided. These new methods rely exclusively on the order structure of the data sets. Hence, the novel tests are stable under monotone transformations of the time series and robust against small perturbations or measurement errors. The standard asymptotic distributions are given, and their finite sample behavior under linear and non-linear departures from the null of independence are studied. Moreover, it is proved that under mild conditions, any ordinal-pattern-based test is nuisance free, which is appealing for modeling, as these tests can eventually be used as misspecification tests. This property is also analyzed for finite samples and illustrated through an empirical application. Much of the discussion is based on a detailed combinatorial analysis of ordinal pattern probabilities.

Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.

This paper proposes two new nonparametric tests for independence between time series. Both tests are based on symbolic analysis, specifically on symbolic correlation integral, in order to be robust to potential unknown nonlinearities. The first test is developed for a scenario in which each considered time series is independent and therefore the interest is to ascertain if two internally independent time series share a relationship of an unknown form. This is especially relevant as the test is nuisance parameter free, as proved in the paper. The second proposed statistic tests for independence among variables, allowing these time series to exhibit within-dependence. Monte Carlo experiments are conducted to show the empirical properties of the tests.

We propose a statistical procedure to determine if a spatial structure that is observed in the data is generated by a deterministic (even chaotic) spatial process, rather than by a stochastic process. This procedure can be used as a speci cation test. It is robust against nonlinearity and nonstationarity and can complete the toolbox for testing diagnosis as well. The advantages of the presented methods are high power, simplicity, and ease and ample applicability for tests to be conducted, provided that weak conditions are required. Herein, we conduct several simulations to evaluate the performance of our procedure on well-known spatial processes and in situations where standard tests for spatial autocorrelation fail to detect spatial dependence. Guidelines for using the technique are also provided herein.

Real phenomena from different areas of Life Sciences can be described by complex networks, whose structure is usually determining their intrinsic dynamics. On the other hand, Dynamical Systems Theory is a powerful tool for the study of evolution processes in real situations. The concept of global attractor is the central one in this theory. In the last decades there has been an intensive research in the geometrical characterization of global attractors. However, there still exists a weak connection between the asymptotic dynamics of a complex network and the structure of associated global attractors. In this paper we show that, in order to analyze the long-time behavior of the dynamics on a complex network, it is the topological and geometrical structure of the attractor the subject to take into account. In fact, given a complex network, a global attractor can be understood as the new attracting complex network which is really describing and determining the forwards dynamics of the phenomena. We illustrate our discussion with models of differential equations related to mutualistic complex networks in Economy and Ecology.

This article analysed the long-term value creation or destruction in mergers and acquisitions (M&A) in the telecommunications sector and the role of language in these types of operations. After characterising the telecommunications market and discussing and selecting the most suitable long-term methodology, an econometric analysis of telecommunications M&A from 1995 to 2010 was conducted. The research focused on telephone companies or operators as the leading players in the sector. Based on this econometric analysis, we found clear evidence of value destruction (2000–2010) when the sector is analysed as a whole, and we found evidence of value creation when the companies involved use the same language. Different levels of value creation or value destruction were found, depending on the specific language of the operation. This econometric study is the first study devoted to long-term value creation in telecommunications after the liberalisation of the telecommunications market and the first study to quantitatively analyse the role of language in telecommunications M&A.

This article presents a dynamic growth model with energy as an input in the production function. The available stock of energy resources is ordered by a quality parameter based on energy accounting: the “Energy Return on Energy Invested” (EROI). To our knowledge this is the first paper where EROI fits in a neoclassical growth model (with individual utility maximization and market equilibrium), setting the economic use of “net energy analysis” on firmer theoretical ground. All necessary concepts to link neoclassical economics and EROI are discussed before their use in the model, and a comparative static analysis of the steady states of a simplified version of the model is presented, Este artículo presenta un modelo de crecimiento dinámico con la energía como un input en la función de producción. El stock de recursos energéticos disponibles se ordena por un parámetro de calidad basado en la contabilidad energética: la tasa de retorno energético (TRE). Por lo que sabemos esta es la primera vez que la TRE encaja en modelo de crecimiento neoclásico (con maximización individual de la utilidad y equilibrio de mercado) estableciendo así el uso del “análisis de rendimiento energético" sobre una base teórica más firme. Todos los conceptos necesarios para enlazar la economía neoclásica con la TRE se discuten antes de ser usados en el modelo, y se presenta un análisis comparativo del estado estacionario de una versión simplificada del modelo

This paper analyzes several academic syllabuses of degrees in economics and business administration that have been obtained from different universities within the framework of the EEES. Cluster analysis has been used to understand how universities are related in function of lectured contents., El presente trabajo es el resultado del análisis llevado a cabo al estudiar los programas de las distintas universidades que ofertan títulos de Economía, Ade o similares en el nuevo Espacio Europeo de Educación Superior. A partir de los datos obtenidos en la página del Ministerio de Educación relativa a las titulaciones, y de los programas y planes de estudios publicados en las webs de las distintas universidades, hemos aplicado el análisis de conglomerados para ver como se relacionan las distintas universidades en función de los contenidos incluidos.

In the present paper we construct a new, simple and powerful test for independence by using symbolic dynamics and permutation entropy as a measure of serial dependence. We also give the asymptotic distribution of an affine transformation of the permutation entropy under the null hypothesis of independence. An application to several daily financial time series illustrates our approach.