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Modelos multinomiales: un análisis de sus propiedades

Listed author(s):
  • Arlen Yahir Guarin Galeano


  • Andrés Ramírez Hassan


  • Juan Felipe Torres


Resumen:En el presente trabajo se desarrolla un análisis de las propiedades de los modelos Multinomiales a través de distintos procesos de simulación; lo anterior se realizó asumiendo tanto el cumplimiento de los supuestos subyacentes de los mecanismos de estimación como el incumplimiento de los mismos. Igualmente se analizó el comportamiento de los estimadores bajo diferentes escenarios de tamaño muestral. Se encontró que bajo un modelo correctamente especificado y tamaños muestrales superiores a 200 observaciones, se cumplen las propiedades de insesgadez y consistencia, mientras que la incorrecta especificación de la distribución del proceso lleva a estimaciones sesgadas e inconsistentes; de igual forma se encontró que en tamaños muestrales pequeños y bajo modelos Condicionales se pierden las propiedades que una buena especificación del proceso suele generar, hallándose aún más inestabilidad cuando la estimación es llevada a cabo con la metodología Probit.Abstract:This paper develops an analysis of Multinomial models through simulation; this was done under correct and incorrect assumptions on the data generating process. Also, it was analyzed the performance of the models under different sample sizes. It was found that a correct specified model with samples of 200 or more observations achieves estimators which are unbiased and consistent, while incorrect assumptions about the data generating process causes biased and inconsistent estimators. On the other hand, conditional models with small sample sizes imply bad statistical properties, especially when Probit models are estimated.

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Paper provided by UNIVERSIDAD EAFIT in its series DOCUMENTOS DE TRABAJO CIEF with number 010604.

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Length: 25
Date of creation: 12 Jun 2011
Handle: RePEc:col:000122:010604
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  1. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, December.
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