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by Daniel José Reyes Valero (Editor), Andrés Eduardo Reyes Polanco (Author)
El texto que se presenta busca dar un fundamento sobre el an lisis de series de tiempo a aquellos profesionales que requieren de la aplicaci n de estas t cnicas. Est dirigido a Ingenieros, Economistas, Actuarios, Administradores y cualquier otra profesi n que requiere un conocimiento suficiente para aplicar las t cnicas de pron sticos en su campo. Los modelos que se tratan en este texto introductorio son tanto univariantes como multivariantes en el dominio del tiempo, haciendo nfasis m s en las aplicaciones que en el desarrollo te rico del an lisis de series de tiempo, pero tratando de no descuidar este aspecto. Para la lectura del texto se requiere conocer los t picos relacionados con c lculo matricial y diferencial, inferencia estad stica y c lculo de probabilidades a un nivel b sico a intermedio.El texto contiene los siguientes cap tulos: Cap tulo I: En este cap tulo se da la introducci n de los conceptos tales como: definici n de una serie de tiempo univariante y los primeros ejemplos de series de tiempo multivariantes. Conceptos de estacionariedad, invertibilidad ergodicidad, teorema de Wold. Adem s, se repasa algunos m todos de estimaci n de los par metros tales como: m xima verosimilitud, bayesianos, no param tricos, m nimos cuadrados lineal y no lineal, m todos exactos y condicionados. Cap tulo II: Trata de una introducci n de procesos estoc sticos lineales, funciones de autocorrelaci n ordinaria o simple y parcial. Ecuaciones de Yule Walker, AR(p), MA(q), y ARIMA(p, d, q). Metodolog a de Box-Jenkins. Cap tulo III: Se estudia, estacionalidad ARIMA(p, d, q)(P, D, Q). El problema de las ra ces unitarias. Contraste de Dickey-Fuller y sus aplicaciones. Contraste de Phillips-Perron. Variable de intervenci n y valores at picos. Cap tulo IV: Trata sobre modelos de series de tiempo no lineales, modelo bilineal. Modelos no lineales en media y varianza condicionales: Modelos ARCH y GARCH. Estimaci n, Contrastes de hip tesis. Modelos relacionados ARCH-M, TARCH, AR-ARCH. INGARCH, Contraste: DBS, Coeficiente de Hurt, modelo ARFIMA. Problemas. Cap tulo V: Trata muy brevemente de series de tiempo multivariante, definici n de series de tiempo multivariante uniecuacional: ARMAX, definici n de series de tiempo vectoriales VAR(p). Cointegraci n, estimaci n, contraste y Vectores Autoregresivos VAR: selecci n, estimaci n, validaci n y pron sticos. VECM. En todos los cap tulos se dan ejemplos reales usando SAS(c) y SPSS(c) y terminan con un conjunto de problemas y observaciones.
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