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    <dc:publisher>Economics: The Open-Access, Open Assessment E-Journal</dc:publisher>
    <dc:publisher>http://www.economics-ejournal.org</dc:publisher>
    <dc:language>en</dc:language>

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<dc:creator>Christian B. Hansen</dc:creator>
<dc:creator>James B. McDonald</dc:creator>
<dc:creator>Panayiotis Theodossiou</dc:creator>
<dc:title>Some Flexible Parametric Models for Partially Adaptive Estimators of Econometric Models</dc:title>
<dc:date>2007-07-09</dc:date>
<dc:description>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.</dc:description>
<dc:identifier>http://www.economics-ejournal.org/economics/journalarticles/2007-7</dc:identifier>
<dc:subject>JEL C13</dc:subject>
<dc:subject>JEL C14</dc:subject>
<dc:subject>JEL C15</dc:subject>


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