Formación de Recurso Humano para la Ctel: Proyectos de investigación y desarrollo
Producción de estadísticas oficiales para el departamento de Cundinamarca basada en estimación en áreas pequeñas
Fecha
2017-10Registro en:
Ortiz Rico, A. F. (2017). Producción de estadísticas oficiales para el departamento de cundinamarca basada en estimación en áreas pequeñas
Autor
Ortiz Rico, Andrés Felipe
Institución
Resumen
The estimation in small areas is a methodology used since the 70s by
the statistical offices of countries such as Canada, the United States, England, Spain,
Mexico, Australia, among others, some examples are the SAIPE1 project in the United States
which seeks to generate income estimates and poverty indicators for districts
schools, counties and states. The LAUS2 program produces monthly and annually
estimates for the unemployment rate, for regions, states, counties, areas
metropolitan areas and cities and the SAMHSA3 initiative that estimates abuse in the consumption of
psychoactive substances in metropolitan areas in the United States.
In Colombia, official statistics are currently not produced using estimation in
small areas; reason why, this project aims to deepen the application of
This methodology for the production of official statistics. The project is presented as
a second phase of the FODEIN project approved in 2017 and entitled "Analysis of
the living conditions of the municipalities of Cundinamarca "in charge of them
authors of this proposal; in which, progress was made in a first application of the methodology
for estimating unemployment rates and average income levels for municipalities
of Cundinamarca, in this second phase, it is expected to optimize the technique used for the
production of these statistics, exploring the adjustment of statistical models that
consider the spatial variability (very common when working at the municipal level) and
refining the models used from the macro-economic point of view, including
information specific to the context of the analysis of unemployment rates and income levels that
allow to obtain optimal predictions of these indicators.