{"id":660,"date":"2020-06-27T10:55:41","date_gmt":"2020-06-27T10:55:41","guid":{"rendered":"https:\/\/ricovictor.com\/?p=660"},"modified":"2020-06-28T18:14:50","modified_gmt":"2020-06-28T18:14:50","slug":"econometria-de-series-de-tiempo-cointegracion","status":"publish","type":"post","link":"https:\/\/ricovictor.com\/index.php\/2020\/06\/27\/econometria-de-series-de-tiempo-cointegracion\/","title":{"rendered":"Econometr\u00eda de series de tiempo: Cointegraci\u00f3n en Stata."},"content":{"rendered":"\t\t
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Cuando se trabaja con series de tiempo<\/strong> para realizar pron\u00f3sticos econ\u00f3micos o financieros, parte importante\u00a0 de lo que se que se debe revisar es si las series de tiempo son estacionarias (o al menos d\u00e9bilmente estacionarias)<\/strong> o no estacionarias<\/a>. Si una serie de tiempo es no estacionaria, los pron\u00f3sticos no ser\u00e1n acertados. Claro est\u00e1 que hay otras t\u00e9cnicas que se pueden utilizar cuando tu serie de tiempo no es estacionaria. Sin embargo en esta ocasi\u00f3n el tema que se va a abordar es la cointegraci\u00f3n<\/strong><\/a>.<\/p>

La cointegraci\u00f3n<\/strong> se refiere al an\u00e1lisis de regresi\u00f3n lineal con variables de series de tiempo que no son estacionarias, y por lo cu\u00e1l los resultados no tendr\u00edan validez o significancia estad\u00edstica<\/strong>. Lo anterior implica que se trataria de una regresion espuria o sin sentido entre las variables. Sin embargo es posible que estas variables se puedan someter a la diferenciaci\u00f3n<\/strong> para conseguir la estacionariedad y puedan compartir una tendencia en com\u00fan, s\u00ed esto es as\u00ed,\u00a0 la relaci\u00f3n entre ambas no ser\u00eda espuria o insignificante. Es por ello que se pueden realizar pruebas de cointegraci\u00f3n para saber si existe una relaci\u00f3n de largo plazo, o de equilibrio<\/strong> entre las variables.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\ud83d\udc49 En resumen, una manera de saber si las variables est\u00e1n cointegradas<\/strong> o no, es estimando el t\u00e9rmino del error<\/strong> y revisar si \u00e9ste es estacionario o no. Si encuentras que el error es estacionario, se puede decir que las variables est\u00e1n cointegradas, de lo contrario se tratar\u00eda, efectivamente, de una relaci\u00f3n espuria. \u00c9sto puede ser muy \u00fatil en campos como las finanzas<\/a> para estudiar el comportamiento de los activos financieros<\/strong> y sus relaciones.\u00a0<\/p>

Por ejemplo en el mercado de acciones<\/a> o en el mercado de Divisas(Forex)<\/strong> donde los movimientos de los pares de divisas est\u00e1n muy interconectados.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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El procedimiento que debemos seguir para detectar la estacionariedad en los errores y por tanto determinar si existe o no cointegraci\u00f3n es el siguiente:<\/p>

Paso 1. Correr la regresi\u00f3n<\/strong><\/p>

Paso 2. Calcular el t\u00e9rmino del error,<\/strong><\/p>

Paso 3. Correr una regresi\u00f3n con el termino del error diferenciado y un rezago.<\/strong><\/p>

Paso 4. Hacer la prueba de ra\u00edz unitaria al error para saber si es estacionario o no.<\/strong><\/p>

En caso de que el error no sea estacionario, puedes incluir la variable de tendencia para conseguirlo.<\/p>

\ud83e\uddd0\ud83e\uddd0 Para que puedas ver todo con m\u00e1s claridad en el siguiente v\u00eddeo<\/strong> tienes un ejemplo pr\u00e1ctico usando el software Stata,<\/strong> que te servir\u00e1 para entender mucho mejor todo el procedimiento.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Si te interesa la econometr\u00eda y la estad\u00edstica aqu\u00ed tienes el canal de Youtube de Lul\u00fa Cuellar ,donde encontrar\u00e1s muchos m\u00e1s v\u00eddeos y tutoriales \ud83d\ude00\ud83d\ude00<\/strong><\/em><\/p>

https:\/\/www.youtube.com\/channel\/UC25LYleJ9taa5CTcEGvE_0A\/featured<\/strong><\/em><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Cuando se trabaja con series de tiempo para realizar pron\u00f3sticos econ\u00f3micos o financieros, parte importante  de lo que se que se debe revisar es si las series de tiempo son estacionarias (o al menos d\u00e9bilmente estacionarias) o no estacionarias. Si una serie de tiempo es no estacionaria, los pron\u00f3sticos no ser\u00e1n acertados. Claro est\u00e1 que …<\/p>\n

Econometr\u00eda de series de tiempo: Cointegraci\u00f3n en Stata.<\/span> Leer m\u00e1s »<\/a><\/p>\n","protected":false},"author":3,"featured_media":665,"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":"","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 center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[2],"tags":[82],"yoast_head":"\nEconometr\u00eda de series de tiempo: Cointegraci\u00f3n en Stata. - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ricovictor.com\/index.php\/2020\/06\/27\/econometria-de-series-de-tiempo-cointegracion\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Econometr\u00eda de series de tiempo: Cointegraci\u00f3n en Stata. - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas\" \/>\n<meta property=\"og:description\" content=\"Cuando se trabaja con series de tiempo para realizar pron\u00f3sticos econ\u00f3micos o financieros, parte importante  de lo que se que se debe revisar es si las series de tiempo son estacionarias (o al menos d\u00e9bilmente estacionarias) o no estacionarias. Si una serie de tiempo es no estacionaria, los pron\u00f3sticos no ser\u00e1n acertados. 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