{"id":11882,"date":"2001-02-07T00:00:00","date_gmt":"2001-02-07T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/prediccion-bootstrap-en-series-temporales\/"},"modified":"2001-02-07T00:00:00","modified_gmt":"2001-02-07T00:00:00","slug":"prediccion-bootstrap-en-series-temporales","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/prediccion-bootstrap-en-series-temporales\/","title":{"rendered":"Predicci\u00f3n bootstrap en series temporales"},"content":{"rendered":"<h2>Tesis doctoral de <strong>  Pascual Caneiro Lorenzo Jos\u00e9 <\/strong><\/h2>\n<p>Se desarrollan nuevos m\u00e9todos de remuestro para estimar densidades e intervalos de predicci\u00f3n que presenten un buen comportamiento para series temporales con innovaciones con colas pesadas, asimetr\u00edas o alg\u00fan tipo de contaminaci\u00f3n. La predicci\u00f3n de valores futuros considerados se basa en modelos univariantes de series temporales, tanto en modelos lineales autorregresivos integrados de medias m\u00f3viles (arima), como en modelos no lineales autorregresivos condicionalmente heteroced\u00e1sticos generalizados (garch). Se motiva la necesidad de obtener intervalos y\/o densidades de predicci\u00f3n en lugar de utilizar \u00fanicamente la informaci\u00f3n proporcionada por las predicciones puntuales. Se propone un nuevo esquema de remuestreo para obtener densidades e intervalos de predicci\u00f3n de valores futuros para series temporales generadas por procesos arima.  se analiza el posible impacto que la estimaci\u00f3n de los par\u00e1metros del modelo tiene sobre las estimaciones de las densidades de predicci\u00f3n. Mediante un extenso estudio de simulaci\u00f3n, se observa la importancia de incluir en las densidades de predici\u00f3n la incertidumbre debida a la estimaci\u00f3n de los par\u00e1metros del modelo cuando el tama\u00f1o muestra es peque\u00f1os o moderado, y cuando se utiliza la metodolog\u00eda boostrap para estimar densidades de predicci\u00f3n de variables despu\u00e9s de que un modelo arima ha sido ajustado a una transformaci\u00f3n de las mismas. Se estiman las densidades de predicci\u00f3n de rendimientos y volatilidades para series temporales generadas por la clase de modelos garch.  el buen comportamiento presentado en los experimentos de monte carlos por los distintos m\u00e9todos bootstrap propuestos se ilustra mediante su implementaci\u00f3n para obtener intervalos de predicci\u00f3n con varios conjuntos de datos reales. las series con las que se ha trabajado son: el \u00edndice de producci\u00f3n industrial italiano, los niveles de una hormona de la menstruaci\u00f3n medios en una mujer sana, los datos de vent<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Predicci\u00f3n bootstrap en series temporales<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Predicci\u00f3n bootstrap en series temporales <\/li>\n<li><strong>Autor:<\/strong>\u00a0  Pascual Caneiro Lorenzo Jos\u00e9 <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Carlos III de Madrid<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 02\/07\/2001<\/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>Juan Romo Urroz<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: daniel Pe\u00f1a s\u00e1nchez de rivera <\/li>\n<li>Ana Justell eusebio (vocal)<\/li>\n<li>Antonio Garc\u00eda ferrer (vocal)<\/li>\n<li>Fernando Tusell palmer (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Pascual Caneiro Lorenzo Jos\u00e9 Se desarrollan nuevos m\u00e9todos de remuestro para estimar densidades e intervalos de predicci\u00f3n [&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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