La mejora de las predicciones con la inclusión de una variable agregada: aplicación a las tasas de variación del PIB de una muestra de economías europeas

El presente trabajo ilustra empíricamente cómo se pueden mejorar las predicciones de las tasas de variación, TV, del output real, PIB, de una muestra de países europeos. Esta mejora ha sido posible gracias al acceso a una base de datos de la Universidad de Groningen, que nos ha permitido calcular las tasas de variación del total de output de los países considerados. Cuando incluimos la tasa de variación total en los modelos AR(3), modelo autorregresivo con tres retardos de la tasa de variación del output de cada país, hemos observado una mejora substancial en las predicciones anuales de de las tasas de variación del output real de cada país.The present work empirically illustrates how the forecast of the annual output growth rates (GR) (where the output is the real gross domestic product (GDP)), from a sample of European countries could be improved. This improvement has been possible by means of using a database that has let us to calculate the aggregated GR of the GDP of the countries sample. When we include the common GR in the AR(3) model of each country’s data, we have observed a substantially improving of one-step-ahead forecasts of GR, due to this common variabl

A generalization of Jeffreys' rule for non regular models

We propose a generalization of the one-dimensional Jeffreys’ rule in order to obtain non informative prior distributions for non regular mod- els, taking into account the comments made by Jeffreys in his arti- cle of 1946. These non informatives are parameterization invariant and the Bayesian intervals have good behavior in frequentist inference. In some important cases, we can generate non informative distributions for multi-parameter models with non regular parameters. In non regular models, the Bayesian method offers a satisfactory solution to the infer- ence problem and also avoids the problem that the maximum likelihood estimator has with these models. Finally, we obtain non informative dis- tributions in job-search and deterministic frontier production homoge- nous model

Effect of shear processing on the linear viscoelastic behaviour and microstructure of bitumen/montmorillonite/MDI ternary composites

Polymer modified bitumens (PMBs) have largely been utilized as a construction material. However, lack of affinity between bitumen and polymer leads to phase separation, and eventually, performance depletion. In this paper, alternative formulations of bitumen with an organically-modified montmorillonite (OMMT) Cloisite 20A® and polymeric methylene diphenyl diisocyanate (MDI) were prepared by melt blending. Their comprehensive rheological characterization evidenced improved linear viscoelastic properties when OMMT is added, revealing a noticeable structural reinforcement and thermal stability. Rheological data also showed that MDI-involved reactions control the composite end properties, being greatly influenced by the shear conditions applied

A comparison between maximum likelihood and Bayesian estimation of stochastic frontier production models

In this paper, the finite sample properties of the maximum likelihood and Bayesian estimators of the half-normal stochastic frontier production function are analyzed and compared through a Monte Carlo study. The results show that the Bayesian estimator should be used in preference to the maximum likelihood owing to the fact that the mean square error performance is substantially better in the Bayesian framewor

Sobre la capacidad de separar los dos errores en el modelo de frontera estocástica normal/half-normal

In this paper, a simulation experiment is carried out in the framework of the normal/half -normal stochastic frontier model in order to analyse its ability to disentangle the two types of errors that form the composite error. According to the results obtained through the mean bias and the mean squared error of the parameters and efficiencies, and via Spearman rank correlation between actual and estimated efficiencies, a good performance of the model is only obtained when considering medium -sized or large samples and the variance of the inefficiencies highly contributes to that of the composite error. The problems of wrong skewness and absence of random error are also addressed. The influence on the results of selecting a wrong distribution for the inefficienc y term is also analysedEn este artículo, se lleva a cabo un experimento de simulación en el contexto del modelo con frontera estocástica normal/half -normal para analizar su capacidad de separar los dos tipos de error que forman el error compuesto. Según los resultados obtenidos a través del sesgo medio y el error cuadrático medio de los parámetros y las eficiencias, y mediante el coeficiente de correlación por rangos de Spearman entre las eficiencias reales y las estimadas, se obtiene un buen comportamiento del modelo solo cuando se consideran muestras de tamaño me diano o grande y la varianza de las ineficiencias contribuye de forma muy importante a la del error compuesto. Los problemas de la asimetría errónea y de la ausencia de errores aleatorios también son abordados. La influencia en los resultados de selecciona r una distribución errónea para el término de ineficiencia también se analiz

Bayesian estimation of the half-normal regression model with deterministic frontier

A regression model with deterministic frontier is considered. This type of model has hardly been studied, partly owing to the difficulty in the application of maximum likelihood estimation since this is a non-regular model. As an alternative, the Bayesian methodology is proposed and analysed. Through the Gibbs algorithm, the inference of the parameters of the model and of the individual efficiencies are relatively straightforward. The results of the simulations indicate that the utilized method performs reasonably wel

Phylogeny of Agavaceae Based on ndhF, rbcL, and its Sequences

Great advances have been made in our understanding of the phylogeny and classification of Agavaceae in the last 20 years. In older systems Agavaceae were paraphyletic due to overemphasis of ovary position or habit. Discovery of a unique bimodal karyotype in Agave and Yucca eventually led to a reexamination of concepts and relationships in all the lilioid monocots, which continues to the present day. Developments in cytogenetics, microscopy, phylogenetic systematics, and most recently DNA technology have led to remarkable new insights. Large-scale rbcL sequence studies placed Agavaceae with the core Asparagales and identified closely related taxa. Analysis of cpDNA restriction sites, rbcL, and ITS nrDNA sequences all supported removal of Dracaenaceae, Nolinaceae, and clarified relationships. Agavaceae s.s. presently consists of Agave, Beschorneria, Furcraea, Hesperaloe, Hesperoyucca, Manfreda, Polianthes, Prochnyanthes, and Yucca. In this paper we analyze recently obtained ndhF sequence data from Agavaceae and Asparagales and discuss the implications for classification. Parsimony analysis of ndhF data alone resolves most genera of Agavaceae and supports the inclusion of Camassia, Chlorogalum, Hesperocallis, and Hosta within Agavaceae s.l. Analysis of combined ndhF and rbcL data sets of selected Asparagales results in better resolution and stronger bootstrap support for many relationships. Combination of all available ndhF, rbcL, and ITS data in a single analysis results in the best resolution currently available for Agavaceae s.l. Implications for classification schemes past and present are discussed