Este documento analiza diferentes estadísticos basados en los residuos para la hipótesis nula de no cointegración utilizando MCG para eliminar los componentes determinísticos. Las distribuciones asintóticas son simuladas para los casos donde un intercepto y un intercepto y una tendencia son incluidos en la ecuación de cointegración. Los resultados muestran que las distribuciones asintóticas dependen del número de regresores (variables xt), el número y clase de componentes determinísticos y un parámtro de nuisamce R2 que mide la correlación de largo plazo entre los regresores xt y la variable yt. Los resultados muestran que MCG permiten obtener más potencia que el uso de MCO. Esto es más claro para valores de R2 menores que 0:4 y un solo regresor xt. Para valores mayores de R2 los denominados estadísticos ECR son mejores para cualquier número de regresores. En particular ...

Extendemos los estadísticos tipo M para una raíz unitaria analizados por Perron y Ng (1996) y Ng y Perron (2001) al caso donde se permite que el cambio en la función de tendencia ocurra en un punto desconocido. Estos estadísticos (MGLS) adoptan el enfoque GLS para eliminar la tendencia desarrollado por Elliott et al. (1996) (ERS) siguiendo los resultados de Dufour y King (1991). Siguiendo a Perron (1989), consideramos dos modelos: uno que permite un cambio en la pendiente y otro que permite tanto un cambio en el intercepto como en la pendiente. Derivamos las distribuciones asintóticas así como el estadístico óptimo factible en un punto de la hipótesis alternativa (PT GLS) sugerido por ERS. También computamos el parámetro de no centralidad utilizado por el enfoque GLS local a la unidad con el fin de eliminar la tendencia que permite que el estadístico PT GLS tenga 50% de poten...

We extend the class of M-tests for a unit root analyzed by Perron and Ng (1996) and Ng and Perron (1997) to the case where a change in the trend function is allowed to occur at an unknown time. These tests M(GLS) adopt the GLS detrending approach of Dufour and King (1991) and Elliott, Rothenberg and Stock (1996) (ERS). Following Perron (1989), we consider two models: one allowing for a change in slope and the other for both a change in intercept and slope. We derive the asymptotic distribution of the tests as well as that of the feasible point optimal tests PT(GLS) suggested by ERS. The asymptotic critical values of the tests aretabulated. Also, we compute the non-centrality parameter used for the local GLS detrending that permits the tests to have 50% asymptotic power at that value. We show that the M(GLS) and PT(GLS) tests have an asymptotic power function close to the power envelope. ...

We extend the class of M-tests for a unit root analyzed by Perron and Ng (1996) and Ng and Perron (1997) to the case where a change in the trend function is allowed to occur at an unknown time. These tests M(GLS) adopt the GLS detrending approach of Dufour and King (1991) and Elliott, Rothenberg and Stock (1996) (ERS). Following Perron (1989), we consider two models: one allowing for a change in slope and the other for both a change in intercept and slope. We derive the asymptotic distribution of the tests as well as that of the feasible point optimal tests PT(GLS) suggested by ERS. The asymptotic critical values of the tests aretabulated. Also, we compute the non-centrality parameter used for the local GLS detrending that permits the tests to have 50% asymptotic power at that value. We show that the M(GLS) and PT(GLS) tests have an asymptotic power function close to the power envelope. ...

Climate change detection and attribution have been the subject of intense research and debate over at least four decades. However, direct attribution of climate change to anthropogenic activities using observed climate and forcing variables through statistical methods has remained elusive, partly caused by difficulties to correctly identify the time-series properties of these variables and by the limited availability of methods to relate nonstationary variables. This paper provides strong evidence concerning the direct attribution of observed climate change to anthropogenic greenhouse gases emissions by first investigating the univariate time-series properties of observed global and hemispheric temperatures and forcing variables and then by proposing statistically adequate multivariate models. The results show that there is a clear anthropogenic fingerprint on both global and hemispheric...

Climate change detection and attribution have been the subject of intense research and debate over at least four decades. However, direct attribution of climate change to anthropogenic activities using observed climate and forcing variables through statistical methods has remained elusive, partly caused by difficulties to correctly identify the time-series properties of these variables and by the limited availability of methods to relate nonstationary variables. This paper provides strong evidence concerning the direct attribution of observed climate change to anthropogenic greenhouse gases emissions by first investigating the univariate time-series properties of observed  global and hemispheric temperatures and forcing variables and then by proposing statistically adequate multivariate models. The results show that there is a clear anthropogenic fingerprint on both global and hemis...

Climate change detection and attribution have been the subject of intense research and debate over at least four decades. However, direct attribution of climate change to anthropogenic activities using observed climate and forcing variables through statistical methods has remained elusive, partly caused by difficulties to correctly identify the time-series properties of these variables and by the limited availability of methods to relate nonstationary variables. This paper provides strong evidence concerning the direct attribution of observed climate change to anthropogenic greenhouse gases emissions by first investigating the univariate time-series properties of observed  global and hemispheric temperatures and forcing variables and then by proposing statistically adequate multivariate models. The results show that there is a clear anthropogenic fingerprint on both global and hemis...