{"id":65682,"date":"2018-03-09T22:53:49","date_gmt":"2018-03-09T22:53:49","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/essays-on-nonlinear-time-series-models\/"},"modified":"2018-03-09T22:53:49","modified_gmt":"2018-03-09T22:53:49","slug":"essays-on-nonlinear-time-series-models","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/modelos-econometricos\/essays-on-nonlinear-time-series-models\/","title":{"rendered":"Essays on nonlinear time series models"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Silvestro Di Sanzo <\/strong><\/h2>\n<p>Esta tesis est\u00e1 compuesta por cuatro cap\u00edtulos en los que se abordan diferentes aspectos relacionados con modelos de series temporales no lineales y m\u00e9todos de simulaci\u00f3n. En particular, el primer cap\u00edtulo propone un nuevo modelo de series temporales que puede captar, de forma conjunta, las propiedades de memoria larga y no linealidad de markov, presentes en algunos procesos de series temporales. En el segundo cap\u00edtulo se propone una novedosa t\u00e9cnica que permite caracterizar y contrastar causalidad de granger en modelos de cambio de r\u00e9gimen de markov. El cap\u00edtulo 3 se preocupa por las diferentes causas que dan origen a fluctuaciones de la producci\u00f3n que son persistentes, mientras que el cap\u00edtulo 4 contempla un m\u00e9todo de remuestreo bootstrap para contrastar la propiedad de linealidad usando modelos de cambio de r\u00e9gimen de markov. Estudios recientes han puesto de manifiesto que, en la pr\u00e1ctica, distinguir entre procesos de memoria larga y procesos no lineales es problem\u00e1tico. El objetivo del cap\u00edtulo 1, \u00c2\u00bfpredicci\u00f3n de series de tiempo con memoria larga y cambios de nivel: un enfoque bayesiano?, Es intentar captar ambas caracter\u00edsticas en un \u00fanico modelo de series temporales para as\u00ed poder valorar su importancia relativa. Con este fin, propongo un modelo que permite combinar las caracter\u00edsticas de memoria larga y no linealidad de markov. La t\u00e9cnica conocida por las siglas mcmc (del t\u00e9rmino ingl\u00e9s markov chain monte carlo) se usa para estimar el modelo y evaluar sus predicciones, las cuales se calculan a partir de densidades predictivas bayesianas. Las predicciones as\u00ed obtenidas suponen una mejora significativa con respecto a las que se obtienen con un modelo lineal de memoria larga o un modelo de cambio de r\u00e9gimen de markov.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Essays on nonlinear time series models<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Essays on nonlinear time series models <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Silvestro Di Sanzo <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Alicante<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 27\/06\/2008<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Gabriel P\u00e9rez Quir\u00f3s<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: dulce Contreras bayarri <\/li>\n<li>mateo Ciccarelli (vocal)<\/li>\n<li>Mar\u00eda \u00e1ngeles Carnero fern\u00e1ndez (vocal)<\/li>\n<li>Ana beatriz Galvao ferreira (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Silvestro Di Sanzo Esta tesis est\u00e1 compuesta por cuatro cap\u00edtulos en los que se abordan diferentes aspectos [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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