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Esta investigación busca estudiar el impacto de la bonanza petrolera en Colombia durante el periodo 2004 a 2014, para un grupo de sectores industriales seleccionados. Este efecto es medido a través de los choques en los términos de intercambio recogidos a través de un índice de tasa de cambio real del crudo (ITCR-C). Los resultados de las pruebas realizadas a través de modelos de Vector Autorregresivo (VAR (p)) para cada sector de la industria seleccionado evidencian que sí existe una relación frente a este fenómeno; además se concluye que, a pesar de la respuesta positiva de un pequeño grupo de sectores frente al choque generado por la expansión minera, la crisis económica internacional (2008-2009) logró desestimular completamente el dinamismo industrial, así, como otros factores asociados a la competencia externa, profundizaron este comportamiento. Abstract: This research is aim to study the impact of crude - mining boom in Colombia during the period 2004 – 2014, on a group of selected industries. This economic impact is calculated through of shocks in exchange terms, expressed in a real exchange rate of crude (REER - C). The results based on vector autoregressive models (VAR(p)) for each sector selected, shows the existence of a relation because of this phenomenon; as well as, it concludes that, despite of an initial positive answer of a small group of industrial sectors because of the shocks caused by the miner boom, the international economic crisis (2008 - 2009) discouraged the industrial dynamic, as well as, other variables related to the external competition, deepened this behavior. Maestría

In this thesis, we propose a systematic approach called the doubly adaptive LASSO tailored to time series analysis, which includes four specific methods for four time series models, respectively: The PAC-weighted adaptive LASSO for univariate autoregressive (AR) models. Although the LASSO methodology has been applied to AR models, the existing methods in the literature ignore the temporal dependence information embedded in AR time series data. Consequently, the methods may not reflect the characteristics of underlying AR processes, especially, the lag order of AR models. The PAC-weighted adaptive LASSO incorporates the partial autocorrelation (PAC) into the adaptive LASSO weights. The PAC-weighted adaptive LASSO estimator has asymptotic oracle properties and a Monte Carlo study shows promising results. The PAC-weighted adaptive positive LASSO for autoregressive conditional heteroscedastic (ARCH) models. We have not found any results in the literature that apply the LASSO methodology to ARCH models. The PAC-weighted adaptive positive LASSO incorporates the PAC information embedded in squared ARCH process into adaptive LASSO weights. The word positive reflects the fact that the parameters in ARCH models are non-negative. We introduce a new concept named the surrogate of the second-order approximate likelihood, and propose a modified shooting algorithm to implement the PAC-weighted adaptive positive LASSO computationally. The PAC-weighted adaptive positive LASSO estimator has asymptotic oracle properties and a Monte Carlo study shows promising results. The PLAC-weighted adaptive LASSO for vector autoregressive (VAR) models. Although the LASSO methodology has been applied to building VAR time series models, the existing methods in the literature ignore the temporal dependence information embedded in VAR time series data. Consequently, the methods may not reflect the characteristics of VAR time series data, especially, the lag order of VAR models. The PLAC-weighted adaptive LASSO incorporates the partial lag autocorrelation (PLAC) into the adaptive LASSO weights. The PLAC-weighted adaptive LASSO estimator has oracle properties and Monte Carlo studies show promising results. The PLAC-weighted adaptive LASSO for BEKK vector ARCH (VARCH) models. We have not found any results in the literature that apply the LASSO methodology to VARCH processes. We focus on the BEKK VARCH models. The PLAC-weighted adaptive LASSO incorporates the PLAC information embedded in the squared BEKK VARCH process into the adaptive LASSO weights. We extend the concept of the surrogate of the second-order approximate likelihood, and propose a modified shooting algorithm to implement the PLAC-weighted adaptive LASSO computationally. We conduct a Monte Carlo study and have preliminary results from the study. Keywords: Time series, financial time series, data mining, oracle property, LASSO, adaptive LASSO, doubly adaptive LASSO, positive LASSO, PAC-weighted adaptive LASSO, PAC-weighted adaptive positive LASSO, PLAC-weighted adaptive LASSO, autoregressive, AR(P), autoregressive conditional heteroscedastic, ARCH(q), vector autoregressive, multivariate autoregressive, VAR(p), vector ARCH, multivariate ARCH, VARCH(q), analytical score, analytical Hessian, quadratic approximation, surrogate to approximate likelihood, S\&P 500, Nikkei.