<?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>2076-054X</journal-id>
<journal-title><![CDATA[Población y Desarrollo ]]></journal-title>
<abbrev-journal-title><![CDATA[Poblac.Desarro.]]></abbrev-journal-title>
<issn>2076-054X</issn>
<publisher>
<publisher-name><![CDATA[FACULTAD DE CIENCIAS ECONÓMICAS- UNA]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2076-054X2023005700047</article-id>
<article-id pub-id-type="doi">10.18004/pdfce/2076-054x/2023.029.57.047</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estimaciones y proyecciones probabilísticas de la esperanza de vida para Argentina. Periodo 2000-2095]]></article-title>
<article-title xml:lang="en"><![CDATA[Probabilistic estimations and projections of life expectancy for Argentina, 2000-2095]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Andreozzi]]></surname>
<given-names><![CDATA[Lucía]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Córdoba Facultad de Ciencias Económicas ]]></institution>
<addr-line><![CDATA[Córdoba ]]></addr-line>
<country>Argentina</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>29</volume>
<numero>57</numero>
<fpage>47</fpage>
<lpage>60</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_arttext&amp;pid=S2076-054X2023005700047&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_abstract&amp;pid=S2076-054X2023005700047&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_pdf&amp;pid=S2076-054X2023005700047&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Las proyecciones demográficas contienen incertidumbre y los modelos probabilísticos se proponen cuantificarla. El presente trabajo desarrolla proyecciones probabilísticas de la Esperanza de Vida al nacer para Argentina para el período 2025-2100 en base al Modelo Jerárquico Bayesiano. Raftery et. al. (2013) desarrollan un modelo que permite estimar la tasa de aumento en la esperanza de vida de un país utilizando datos anteriores de ese país, y también teniendo en cuenta los patrones pasados observados en todos los demás países. Luego Raftery (2014) usa el modelo de brecha femenina para desarrollar la esperanza de vida en varones. Se evalúan para ello tres períodos de ajuste y en base a la selección del más adecuado se analizan los resultados; la esperanza de vida para varones y mujeres, su tendencia y la brecha entre ambas. Finalmente, se comparan los resultados estimados con los datos oficiales de la agencia nacional de estadís- tica. El MJB presenta resultados plausibles junto con una medida de incertidumbre asociada que permite decidir hasta cuando serían confiables o informables los pronósticos, sin embargo, el período base incluye proyecciones como datos de entrada, generando una paradoja de usar proyecciones para proyectar. La diferencia que se presenta en las proyecciones para varones da cuenta de la importancia en el modelo al incluir o no el último punto de la serie de datos. Si bien los modelos probabilísticos representan grandes avances quedan detalles por discutir, como el del período base y su relación con las fuentes oficiales (sean estimaciones o proyecciones).]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Demographic projections contain uncertainty and probabilistic models aim to quantify it. This paper develops probabilistic projections of Life Expectancy at birth for Argentina for the period 2025-2100 based on the Bayesian Hierarchical Model. Raftery et. to the. (2013) develop a model that allows estimating the rate of increase in life expectancy of a country using previous data from that country, and taking into account past patterns observed in all other countries. Then Raftery (2014) uses the female gap model to develop life expectancy in males. For this purpose, three adjustment periods are evaluated and the results are analyzed based on the selection of the most appropriate one; life expectancy for men and women, its trend and the gap between the two. Finally, the estimated results are compared with the official data from the national statistical agency. The BHM presents plausible results together with a measure of associated un- certainty that allows deciding until when the forecasts would be reliable or reportable, however, the base period includes projections as input data, generating a paradox of using projections to project. The difference that occurs in the projections for men shows the importance in the model of including or not the last point of the data series. Although the probabilistic models represent great advances, details remain to be discussed, such as the base period and its relationship with official sources (whether estimates or projections).]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[modelo jerárquico bayesiano]]></kwd>
<kwd lng="es"><![CDATA[esperanza de vida al nacer]]></kwd>
<kwd lng="es"><![CDATA[Argentina]]></kwd>
<kwd lng="en"><![CDATA[bayesian hierarchical model]]></kwd>
<kwd lng="en"><![CDATA[life expectancy at birth]]></kwd>
<kwd lng="en"><![CDATA[Argentina.]]></kwd>
</kwd-group>
</article-meta>
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