Terms of Trade and Output Fluctuations in Colombia

This paper explores the importance of the terms of trade to explain output fluctuations in Colombia, a developing country where almost 60% of the exports correspond to four commodities: oil (32%), coal (17%), coffee (5%) and nickel (2%), and where 80% of its imports are intermediate and capital goods. This research is motivated fundamentally by the particular importance of short run fluctuations in developing economies, the fact that the Colombian terms of trade are procyclical and the current debate in Colombia about eventual economic policies toward sterilization of the effects of changes in commodities prices in a context of an appreciation of the nominal exchange rate. The study includes a time series analysis, for the period 1994-2009 with quarterly data, which follows the Box-Jenkins methodology for an ARMAX model. I find robust evidence that indicates that the quarterly growth of GDP is positively and significantly affected by variations in the terms of trade, which explain 1/3 of GDP growth variability. This result is consistent with the possible outcome of the three-goods model for an open small economy in which the terms of trade can be the source of the aggregate output fluctuations. JEL Categories:

Terms of Trade and Output Fluctuations in Colombia

This paper explores the importance of the terms of trade to explain output fluctuations in Colombia, a developing country where almost 60% of the exports correspond to four commodities: oil (32%), coal (17%), coffee (5%) and nickel (2%), and where 80% of its imports are intermediate and capital goods. This research is motivated fundamentally by the particular importance of short run fluctuations in developing economies, the fact that the Colombian terms of trade are procyclical and the current debate in Colombia about eventual economic policies toward sterilization of the effects of changes in commodities prices in a context of an appreciation of the nominal exchange rate. The study includes a time series analysis, for the period 1994-2009 with quarterly data, which follows the Box-Jenkins methodology for an ARMAX model. I find robust evidence that indicates that the quarterly growth of GDP is positively and significantly affected by variations in the terms of trade, which explain 1/3 of GDP growth variability. This result is consistent with the possible outcome of the three-goods model for an open small economy in which the terms of trade can be the source of the aggregate output fluctuations

The Role of Social Networks on Regulation in the Telecommunication Industry

This paper studies the welfare implications of equilibrium behavior in a market characterized by competition between two interconnected telecommunication firms, subject to constraints: the customers belong to a social network. It also shows that social networks matter because equilibrium prices and welfare critically depend on how people are socially related. Next, the model is used to study effectiveness of alternative regulatory schemes. The standard regulated environement, in which the authority defines interconnection ac cess charges as being equal to marginal costs and final prices are left to the market, is considered as a benchmark. Then, we focus on the performance of two different regulatory interventions. First, access prices are set below marginal costs to foster competition. Second, switching costs are reduced to intensify competition. The results show that the second strategy is more efective to obtain equilibrium prices closer to Ramsey's level.Access charges, social networks, random regular graphs

Módulo de edición para un tutor online de C

Los entornos de aprendizaje virtual son una herramienta en continuo crecimiento hoy en día. Son un complemento ideal al aprendizaje tradicional y presencial y, sin duda, una de las mayores ventajas de este sistema es la posibilidad que otorga al alumno para imponerse su propio ritmo de trabajo. Esta personalización e interactividad fomentan que los estudiantes sean los responsables de su propio aprendizaje. Para que esto sea posible estas plataformas deben permitir un acceso completo y estructurado a los contenidos que se desean impartir, por ello la gestión de contenidos tiene un peso importante en el desarrollo de este tipo de aplicaciones. Mediante la realización de este Trabajo de fin de Grado, se ha conseguido añadir un módulo extra a una aplicación de enseñanza online. La aplicación en cuestión es un curso de introducción a la programación en lenguaje C, que proporciona distintos contenidos y actividades dirigidas a alumnos de estudios de Ingeniería informática. Actualmente la aplicación se encuentra en funcionamiento y algunas secciones ya se encuentran plenamente funcionales, como la gestión de los usuarios, o el módulo de visualización de estadísticas sobre el rendimiento de los alumnos. Otras, en cambio, aún no han sido desarrolladas y se espera que se añadan en un futuro sin afectar al correcto funcionamiento de la aplicación. En concreto, la parte que atañe a este Trabajo de Fin de Grado se corresponde con la edición y la ampliación del temario y actividades incluidos en la aplicación. Los contenidos se encuentran divididos en distintas unidades con un nivel de dificultad progresivamente más elevado. Cada una de estas unidades pone a disposición del alumno una serie de explicaciones teóricas acompañadas de ejercicios a los cuales el alumno debe responder y son corregidos de manera automática en la aplicación. También se incluye en cada una de las páginas un código en lenguaje C que es posible compilar, ejecutar y corregir, permitiendo al alumno realizar las modificaciones oportunas sobre el mismo. El objetivo principal de este Trabajo de Fin de Grado consiste en facilitar el trabajo a la hora de añadir contenidos en la aplicación mediante el desarrollo de un nuevo módulo independiente, de modo que el personal docente que la usa pueda realizar estas modificaciones de contenido de una manera eficiente. Esta nueva funcionalidad permitirá a las personas sin conocimientos de programación poder realizar estas tareas a través de un formulario estándar, ya que hasta el momento la manera de agregar o editar estas unidades era realizar modificaciones directamente en el código de la aplicación lo cual es un trabajo laborioso. Otro aspecto importante y esencial de este Trabajo de Fin de Grado es que todo el desarrollo de este módulo se lleve a cabo afectando mínimamente al resto de la aplicación, debe mantenerse funcional en todo momento, y a la vez, el nuevo módulo ha de ser compatible con los actuales que ya se encuentran operativos siendo también un requisito imprescindible que dicho modulo sea ampliable y/o modificable fácilmente en caso de ser necesario.Virtual learning environments are tools in constant growth and development. They are the ideal complement to traditional face-to-face learning methods. One of the greatest advantages of this kind of system is that it allows the students to set their own pace of work. This personalization and interactivity encourage the student to be responsible for their own learning process. For this to be possible these platforms should grant full and structured access to the contents that will be taught, and that is why content management has great importance in the development of these applications. The development of this project has made it possible to add new modules to an existing online learning application. The application is an online training course intended for learning how to program in the C programming language. It provides a variety of different contents areas and activities aimed to students of computer science engineering. At the present time the application is already online and working. Some parts of it, like user management or the stats module are totally functional while other parts are still under development or the development has not yet begun. It is expected that new functionality will be added to the application in the near future without affecting the already working modules and without affecting the proper functioning of the website. Specifically the part that concerns the work explained in this document is that which is related to the edition and addition of new units and exercises included in the application. The contents are divided into units with an increasing difficulty level. Each of these units makes available to the user a series of contents that includes a brief explanation sometimes accompanied by exercises which the user needs to complete and which are automatically corrected in the application. It is also included in each page a section with a C language code that it is possible to modify, compile and execute by the user, allowing the students to test the learned skills. The main goal of this project is to make the task of adding or editing content in the application easier, so the teaching staff can do it more efficiently and without worrying about the errors that can occur when adding new content by editing the source code of each unit and also allowing people without a computer science background to do this task with a simple form. There will not be the need to edit the source code of the application, saving that laborious work. Another essential aspect of the project is to keep the application working while the new module is developing and at the same time, the new module has to be compatible with the old ones that are already operational and fully working. And finally the new parts that this project includes have to be able to expand or modify easily

Deep Gaussian processes using expectation propagation and Monte Carlo methods

Machine learning can be de ned as the application of arti cial intelligence that provides systems with the ability of learning and improve from experience. One of the main research areas in this eld is supervised learning, in which an output variable is predicted from some input variables. These types of problems are divided into several stages. In the rst one, the system is provided with the so-called training data. This data includes both input and (labeled) output variables. After the training phase, the system is ready to make accurate predictions about the values of the output variables that are not labeled. Depending on the type of output variables, we can di erentiate between classi cation (categorical variables) and regression (continuous variables) problems. Gaussian processes are non-parametric machine learning models that present advantages over other models. They provide not only a prediction about the output value, but they also provide the uncertainty of such prediction. It is also not required to make assumptions about the form of the process (function) that generated the data, is enough to include the high-level information (smoothness, periodicity, ...) in the so-called covariance function or kernel that jointly with the mean de ne the model. Although Gaussian processes are robust to over- tting they are limited by the expressiveness of the covariance function. There has been some work trying to extend this traditional model to other more expressive variants, by for example considering more sophisticated covariance functions or integrating the model in more complex probability structures. However, none of these approaches make use of deep architectures. Recently it has been shown that Gaussian processes can overcome these problems and be used as individual units to construct deep networks giving rise to deep Gaussian processes. These models maintain the advantages of single layer Gaussian processes but reduce the hypotheses made about the data, yielding more exible models. Deep Gaussian processes have proven hard to train because exact Bayesian inference is not possible and approximate inference techniques have to be used. Some models that represent the state of the art on deep Gaussian processes research have used some of these techniques like variational inference or approximate variants of the expectation propagation algorithm. In this master's thesis, we present a new machine learning model for inference in deep Gaussian processes models using an approximate inference technique based on Monte Carlo methods and the expectation propagation algorithm. We demonstrate through extensive experiments that our approach provides competitive results even when compared with some state of the art models. Finally, we show that our model scales well with bigger datasets and it is suitable to use the proposed approach to solve Big Data problems. Furthermore, our model presents di erent properties such as being able to capture noise that is dependent on the input and modeling multimodal predictive distributions. Both of these properties, which are not shared with other approximate inference methods, are analyzed in our experiments.El aprendizaje automático puede defi nirse como una aplicación de la inteligencia arti ficial que proporciona a los sistemas la habilidad de aprender y mejorar a partir de la experiencia. Una de las principales áreas de trabajo de este campo es el aprendizaje supervisado, en el que se intenta predecir una variable de salida a partir de unas variables de entrada. Este tipo de problemas se dividen en varias fases. En la primera se provee al sistema de los denominados datos de entrenamiento. Estos datos incluyen tanto las variables de entrada como las de salida (etiquetas). Tras la fase de entrenamiento el sistema está listo para realizar predicciones precisas sobre las variables de salida a partir de las variables de entrada sin etiquetar. Dependiendo del tipo de variables de salida del problema se puede diferenciar entre problemas de clasi ficación (variables categóricas) o problemas de regresión (variables continuas). Dentro del aprendizaje automático, los procesos Gaussianos son modelos no paramétricos que presentan numerosas ventajas con respecto a otros modelos. Al ser métodos bayesianos, proporcionan no solamente una predicción del valor de la variable de salida sino también un grado de incertidumbre para la misma. Tampoco es necesario realizar suposiciones sobre la forma del proceso (función) que generó los datos. Es su ciente con incluir información de alto nivel (suavidad, periodicidad, etc.) en la llamada función de covarianzas o kernel que, junto con la media, de nen el proceso Gaussiano. A pesar de ser métodos robustos, resistentes al sobre-aprendizaje, los procesos Gaussianos quedan limitados por la expresividad de la función de covarianzas. Por ello, se han intentado extender estos modelos tradicionales a variantes más expresivas, por ejemplo, considerando funciones de covarianza más so fisticadas o integrándolos en estructuras probabilísticas más complejas. Sin embargo, ninguno de estos enfoques lleva a arquitecturas profundas. Recientemente se ha mostrado que los procesos Gaussianos pueden superar estos problemas y usarse como unidades individuales para construir redes profundas, dando como resultado procesos Gaussianos profundos. Estos modelos mantienen las ventajas de los procesos Gaussianos estándar, pero reducen las hipótesis realizadas sobre los datos, conduciendo a modelos más flexibles. La contrapartida de estos modelos es que presentan difi cultades a la hora de entrenarlos, pues la inferencia bayesiana exacta no es posible y se han de usar técnicas de inferencia aproximada. Algunos modelos que representan el estado de arte en la investigación sobre procesos Gaussianos profundos han intentado hacer uso de las técnicas de inferencia aproximada, como por ejemplo, Inferencia Variacional, o variantes aproximadas del algoritmo de propagación de esperanzas. En este trabajo de fin de máster presentamos un nueva técnica para realizar inferencia en procesos Gaussianos profundos mediante el uso de un método de inferencia aproximada basado en métodos Montecarlo y el algoritmo de propagación de esperanzas. Demostramos mediante exhaustivos experimentos que nuestro enfoque proporciona resultados competitivos al nivel de otros modelos que representan el estado del arte. Por ultimo mostramos que el modelo propuesto escala bien para conjuntos de datos grandes y su uso es adecuado para la resolución de problemas de Big Data. Además, presenta otras propiedades que analizamos en nuestros experimentos, como la posibilidad de capturar ruido dependiente de los datos de entrada o el modelado de distributiones predictivas multimodales. Estas propiedades no son observadas en otros métodos de inferencia aproximada

Causas y consecuencias de la gran persecución

Sin resume

Algunas formas de financiación de la empresa

En este trabajo de fin de grado encontramos algunas de la gran cantidad de formas de financiación que poseen las empresas españolas. Se ha tratado de diferenciar los métodos más relevantes, dentro de la gran variedad existente. Además, se han estudiado las fuentes de financiación más novedosas, y por las que optan a día de hoy las empresas, con el fin de adaptar a cada caso empresarial sus necesidades de recursos financieros, buscando ante todo la eficiencia.Departamento de Economía AplicadaGrado en Administración y Dirección de Empresa

Community-Based Outdoor Science Education in Chile: A Contribution to Expanding Networks within Pro-Eco-Justice Dispositifs

Chile has been exposed to multiple climatic risks, such as extreme droughts, scarcity of water resources, forest fires, floods, glacial retreats, and others. Impacts of such global phenomena on the region – without any mitigation measures – are likely to be complex, given vulnerability criteria set forth by the United Nations Framework Convention on Climate Change. In this scenario, it is urgent to think about new approaches to change perspectives and practices of science educators in ways that may develop new networks to address the above challenges. For this paper, I present an ongoing community-based outdoor project aimed at promoting awareness, empathy, and activism towards expanding networks within ecojustice dispositifs. The project is co-designed among researchers, park rangers and pre- and in-service teachers from a Chilean university. This paper aimed at answering the following research questions: To what extent might community-based outdoor education promote increased development of pro-ecojustice dispositifs. In this paper, I present one of the first stages of the project where a map and an atlas was used to collect information about socioenvironmental conflicts in Chile. With this information, we planned and developed an outdoor science activity in a national reserve. The research opted for a qualitative approach to understanding design and implementation processes of outdoor education through community-based participatory research. At answering this research question, a socio-environmental conflict is presented as a distributed network of living, nonliving and symbolic actants, based on Actor-Network-Theory, which might affect present and future water management in Santiago, Chile. The main results of the research revealed potential benefits of Actor Network Theory as an obligatory passage point in the problematisation stage of processes developing more ecojustice dispositifs. This ongoing project appears as a potential contribution to expanding networks within eco-justice dispositifs to challenge anthropocentrism and processes of neoliberalisation of nature