<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1665-5346</journal-id>
<journal-title><![CDATA[Revista mexicana de economía y finanzas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. econ. finanz]]></abbrev-journal-title>
<issn>1665-5346</issn>
<publisher>
<publisher-name><![CDATA[Instituto Mexicano de Ejecutivos de Finanzas A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1665-53462015000200105</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[On the econometric modeling of nonlinear relationships: the gumbel regression model]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez Vargas]]></surname>
<given-names><![CDATA[Armando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Márquez Estrada]]></surname>
<given-names><![CDATA[José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Autónoma de México Institute For Economics Research ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Autónoma de México Institute For Economics Research ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2015</year>
</pub-date>
<volume>10</volume>
<numero>2</numero>
<fpage>105</fpage>
<lpage>113</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-53462015000200105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-53462015000200105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-53462015000200105&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Nonlinear relationships among random variables often come out in all fields of economics. The academic debate on how to deal with nonlinearities, from a statistical point of view, has been centered in developing new estimation methods or modifying the specification of the classic linear econometric models. Here, we propose to face this issue by deriving population regression models from conditional distributions with genuine nonlinear conditional means. Such a mathematical procedure guarantees not only a consistent derivation of the conditional mean that gives rise to a nonlinear econometric model, but also a proper analysis of the causal effects among the involved economic variables (i.e., partial effects). Finally, we exemplify the workings of this approach by specifying a nonlinear and heteroskedastic econometric model based on the Gumbel distribution.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Relaciones de tipo no lineal entre variables aparecen de manera frecuente en todos los campos de la economía. El debate académico sobre como modelar dichas relaciones, desde un punto de vista estadístico, ha estado centrado en el desarrollo de nuevos métodos de estimación o en la especificación de los componentes del modelo clásico de regresión lineal. En este artículo proponemos enfrentar dicho problema derivando los modelos de regresión poblacional a partir de funciones de densidad condicionales con medias no-lineales genuinas. Este procedimiento matemático garantiza no sólo una consistente derivación de la media condicional que da origen a un modelo econométrico no lineal, sino también un análisis más apropiado de los efectos causales entre las variables económicas involucradas (efectos marginales). Finalmente, se presenta un ejemplo del funcionamiento de dicho procedimiento mediante la especificación de un modelo econométrico no lineal y heteroscedastico que surge de una distribución Gumbel.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Gumbel Distribution]]></kwd>
<kwd lng="en"><![CDATA[Nonlinear Relationships]]></kwd>
<kwd lng="en"><![CDATA[Conditional Mean]]></kwd>
<kwd lng="en"><![CDATA[Population Regression Model]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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