Mitigando la multicolinealidad con Mínimos Cuadrados Restringidos y la Regresión Alzada
C. García García, R. Salmerón Gómez, C. Garcia Garcia
Cuando en un modelo de regresión lineal existe un grado de multicolinealidad aproximada que afecta al análisis estadístico del mismo, puede ocurrir que no se rechace la hipótesis nula en los contrastes de significación individual al mismo tiempo que se rechaza la hipótesis nula en el contraste de significación conjunta. Para salvar esta contradicción, se propone el uso del estimador por Mínimos Cuadrados Restringidos (MCR), que proporciona estimadores con menor varianza estimada que el de Mínimos Cuadrados Ordinarios (MCO), mitigando así este efecto de la multicolinealdiad aproximada sobre el análisis estadístico del modelo. Concretamente, puesto que la efectividad del estimador por MCR se basa en no rechazar la hipótesis realizada sobre las restricciones de los coeficientes del modelo, la novedad de la aportación realizada radica en la propuesta de utilizar como restricciones sobre los coeficientes del modelo las obtenidas al relacionar los coeficientes del modelo original y alzado.
Keywords: Multicolinealidad, varianza, MCR, regresión alzada
Scheduled
Invited Session Quantitative Methods for Economics and Business.
June 7, 2022 4:50 PM
Grade Hall
Other papers in the same session
F. Prieto, J. M. Sarabia, E. Calderín-Ojeda
R. Salmerón Gómez, C. Garcia Garcia, J. Garcia Perez
A. Rodríguez Sánchez, C. B. Garcia Garcia, R. Salmerón Gómez
C. Garcia Garcia, R. Salmerón Gómez, J. Garcia Perez
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6/8/22
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5/27/22
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5/23/22
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