Please use this identifier to cite or link to this item:
|Title:||Some flexible parametric models for partially adaptive estimators of econometric models||Authors:||Hansen, Christian B.
McDonald, James B.
|Keywords:||Partially Adaptive Estimation;Econometric Models||Issue Date:||2007||Source:||Economics - The Open-Access, Open-Assessment E-Journal, July 2007, pp. 1-20||Link:||http://www.economics-ejournal.org/economics/journalarticles/2007-7||Abstract:||This paper provides a survey of three families of flexible parametric probability density functions (the skewed generalized t, the exponential generalized beta of the second kind, and the inverse hyperbolic sine distributions) which can be used in modeling a wide variety of econometric problems. A figure, which can facilitate model selection, summarizing the admissible combinations of skewness and kurtosis spanned by the three distributional families is included. Applications of these families to estimating regression models demonstrate that they may exhibit significant efficiency gains relative to conventional regression procedures, such as ordinary least squares estimation, when modeling non-normal errors with skewness and/or leptokurtosis, without suffering large efficiency losses when errors are normally distributed. A second example illustrates the application of flexible parametric density functions as conditional distributions in a GARCH formulation of the distribution of returns on the S&P500. The skewed generalized t can be an important model for econometric analysis.||URI:||http://ktisis.cut.ac.cy/handle/10488/1181||Rights:||© 2007 Author(s)||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
Page view(s) 5050
checked on Nov 15, 2018
This item is licensed under a Creative Commons License