<?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>0120-1751</journal-id>
<journal-title><![CDATA[Revista Colombiana de Estadística]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.Colomb.Estad.]]></abbrev-journal-title>
<issn>0120-1751</issn>
<publisher>
<publisher-name><![CDATA[Departamento de Estadística - Universidad Nacional de Colombia.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-17512018000100031</article-id>
<article-id pub-id-type="doi">10.15446/rce.v41n1.61885</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Estimating dynamic Panel data. A practical approach to perform long panels]]></article-title>
<article-title xml:lang="es"><![CDATA[Estimando Datos de panel dinámicos. Un enfoque práctico para abordar paneles largos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Labra]]></surname>
<given-names><![CDATA[Romilio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Torrecillas]]></surname>
<given-names><![CDATA[Celia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Talca Instituto de Innovación Basada en Ciencia ]]></institution>
<addr-line><![CDATA[Talca ]]></addr-line>
<country>Chile</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Europea de Madrid Facultad de Ciencias Sociales y de la Comunicación Departamento de Administración y Dirección de Empresas]]></institution>
<addr-line><![CDATA[Madrid ]]></addr-line>
<country>España</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<volume>41</volume>
<numero>1</numero>
<fpage>31</fpage>
<lpage>52</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-17512018000100031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-17512018000100031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-17512018000100031&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Panel data methodology is one of the most popular tools for quantitative analysis in the field of social sciences, particularly on topics related to economics and business. This technique allows simultaneously dressing individual effects, numerous periods, and in turn, the endogeneity of the model or independent regressors. Despite these advantages, there are several methodological and practical limitations to perform estimations using this tool. There are two types of models that can be estimated with Panel data: Static and Dynamic, the former is the most developed while dynamic models still have some theoretical and practical constraints. This paper focuses precisely on the latter, Dynamic panel data, using an approach that combines theory and praxis, and paying special attention on its applicability on macroeonomic data, specially datasets with a long period of time and a small number of individuals, also called long panels.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La metodología de Datos de Panel es una de las técnicas más usadas para realizar análisis cuantitativos en el ámbito de las ciencias sociales, especialmente en temas relacionados con la economía y los negocios. Su riqueza reside en que esta técnica permite trabajar con varios periodos de tiempo, incorporar los efectos individuales, y a su vez, tratar la endogeneidad. A pesar de estas ventajas, existen diversos obstáculos para su implementación, tanto metodológicos como operativos. Dentro de los tipos de modelos que se pueden estimar con Datos de Panel, los de carácter estáticos han sido los más desarrollados, persistiendo aún carencias teórico-prácticas para los modelos dinámicos. Este artículo pone precisamente su énfasis en estos últimos, aplicando un enfoque que conjuga la teoría y la praxis, y prestando especial atención a su aplicabilidad para datos macroeconómicos, fundamentalmente para paneles que poseen un período de tiempo largo y un número de individuos pequeño.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Dynamic Panels]]></kwd>
<kwd lng="en"><![CDATA[Endogenous Models]]></kwd>
<kwd lng="en"><![CDATA[Overidentification]]></kwd>
<kwd lng="en"><![CDATA[Panel Data]]></kwd>
<kwd lng="en"><![CDATA[Stata]]></kwd>
<kwd lng="en"><![CDATA[xtabond2]]></kwd>
<kwd lng="es"><![CDATA[datos de panel]]></kwd>
<kwd lng="es"><![CDATA[datos de panel dinámicos]]></kwd>
<kwd lng="es"><![CDATA[modelos endógenos]]></kwd>
<kwd lng="es"><![CDATA[sobreidentificación]]></kwd>
<kwd lng="es"><![CDATA[stata]]></kwd>
</kwd-group>
</article-meta>
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