<?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>0186-7202</journal-id>
<journal-title><![CDATA[Estudios Económicos (México, D.F.)]]></journal-title>
<abbrev-journal-title><![CDATA[Estud. Econ. (México, D.F.)]]></abbrev-journal-title>
<issn>0186-7202</issn>
<publisher>
<publisher-name><![CDATA[El Colegio de México A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0186-72022023000100003</article-id>
<article-id pub-id-type="doi">10.24201/ee.v38i1.435</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms]]></article-title>
<article-title xml:lang="es"><![CDATA[Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rincón]]></surname>
<given-names><![CDATA[Ratzanyel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,The University of British Columbia  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Canada</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<volume>38</volume>
<numero>1</numero>
<fpage>3</fpage>
<lpage>68</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0186-72022023000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0186-72022023000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0186-72022023000100003&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: This article addresses the lack of timely information about multidimensional poverty in Mexico. Three machine learning algorithms the LASSO logistic regression, random forest, and support vector machinesare trained with the ENIGH to find generalizable patterns of multidimensional poverty in the raw data. The fitted models are used to classify each individual in the ENOE as poor or non-poor to obtain aggregated poverty rates on a quarterly basis. These estimates are closer to the official levels of multidimensional poverty than the labor poverty measurement and provide an accurate poverty outlook more than a year ahead of the official measure.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Este artículo aborda la falta de información oportuna sobre la pobreza multidimensional en México. Tres algoritmos de aprendizaje de máquinala regresión LASSO logística, el bosque aleatorio y las máquinas de vectores de soporteson entrenados con la ENIGH para encontrar patrones generalizables de pobreza multidimensional en los datos. Los modelos se utilizan para clasificar a cada individuo en la ENOE como pobre o no-pobre para obtener tasas de pobreza trimestrales. Estas estimaciones son más cercanas a los niveles de pobreza multidimensional que la pobreza laboral y brindan una perspectiva precisa sobre la pobreza con más de un año de antelación a la medición oficial.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[multidimensional poverty]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[LASSO logistic regression]]></kwd>
<kwd lng="en"><![CDATA[random forest]]></kwd>
<kwd lng="en"><![CDATA[support vector machines]]></kwd>
<kwd lng="es"><![CDATA[pobreza multidimensional]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje de máquina]]></kwd>
<kwd lng="es"><![CDATA[regresión logística de LASSO]]></kwd>
</kwd-group>
</article-meta>
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