Essays on Spatial Econometrics and Economics

  • Autor: Maryna Makeienko
  • Director/es: Mariano Matilla García, Paloma Úbeda Mollá
  • Defensa: 27/6/2022 - Universidad Nacional de Educación a Distancia
  • Tribunal: Manuel Ruiz Marín, Román Mínguez, Fernando López Hernández
  • Calificación: Sobresaliente cum laude
  • Ver publicaciones relacionadas

Resumen:

La motivación del presente trabajo aflora por la importancia de identificar los componentes permanentes que caracterizan la determinación de aspectos fundamentales microeconómicos, como son los precios. La tesis analiza decisiones basadas en precios, por una parte, el precio de mercado laboral y su implicación en el nivel de desempleo y por otra parte el precio de las viviendas, siendo estos, aspectos claves desde el punto de vista microeconómico.
En esta tesis se desarrolla una aportación metodológica y analítica para la adecuada identificación de los componentes permanentes, que pueden caracterizar un conjunto de datos con referencias espaciales. La metodología desarrollada en esta tesis es eminentemente empírica implementada en dos estudios o casos muy relevantes en la ciencia económica y econométrica. Se lleva a cabo el estudio y análisis del proceso de determinación de los elementos principales que caracterizan las bases de datos espaciales identificando la existencia del componente determinista. Para la consecución de este objetivo global se llevan a cabo análisis diferentes que han conllevado a una subdivisión de la presente tesis en dos partes claramente diferenciadas.
En la primera parte, la tesis está focalizada en la aplicación de las herramientas de econometría espacial clásicas, pero a diferencia de los análisis tradicionales conocidos, se les añade una herramienta de clave para el presente estudio: un proceso de identificación de modelos que permite identificar la tendencia espacial dentro de los datos. En esta sección se subdivide en dos estudios, el primero de ellos analiza la base de datos de precios de viviendas utilizando modelos de regresión clásicos espaciales, splines así como modelos delta, siendo éstos últimos más simples ya que sólo tienen en cuenta la localización. Asociado a la incorporación de los de modelos delta, se cuestiona la necesidad y obligación del uso de la matriz de pesos espaciales asociada a los modelos clásicos de regresión espacial.
El segundo análisis contribuye en la explotación de las técnicas de análisis y cómo se comportan estas en diferentes entornos. Estas técnicas se aplican a los conjuntos de datos que presentan diferentes características y tienen un comportamiento económico diferente. Este enfoque nos permite resaltar las principales diferencias entre los conjuntos de datos donde los modelos delta funcionan mejor que los modelos espaciales clásicos o viceversa.

La segunda parte de la tesis presenta el análisis de los movimientos de empleo transaccionales de la fuerza laboral dentro del mercado de trabajo, teniendo en cuenta las características psicológicas básicas de las personas (Big Five). El objetivo principal era estudiar las relaciones entre los rasgos de personalidad, Big Five, y los diferentes resultados en el mercado laboral, como el éxito de los trabajadores desempleados que buscan trabajo y su éxito en el mercado laboral, medido por una serie de periodos de desempleo. Los resultados sugieren vínculos importantes entre los rasgos de personalidad y el desempleo, y esto está corroborado por los análisis de robustez realizados, sin embargo, diferentes características tienen diferente poder explicativo y diferente tipo de influencia en el estado de desempleo individual observado. Nuestros resultados contribuyen a la discusión sobre la heterogeneidad individual en el desempleo al mostrar que las diferencias en las personalidades pueden explicar partes de las diferencias individuales en la historia laboral.

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Hedonic models for Baltimore, Boston, and Toledo housing prices datasets are revisited, studied (with the new proposed procedures), and compared with standard spatial econometric methodologies. 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It firstly considers testing spatial dependence in the presence of potential leading deterministic spatial components (similar to time-series tests for unit roots in the presence of temporal drift and/or time-trend) and secondly considers how to econometrically model spatial economic relations that might contain unobserved spatial structure of unknown form. Hypothesis testing is conducted with a symbolic-entropy based non-parametric statistical procedure, recently proposed by Garcia-Cordoba, Matilla-Garcia, and Ruiz (2019), which does not rely on prior weight matrices assumptions. It is shown that the use of geographically restricted semiparametric spatial models is a promising modeling strategy for cross-sectional datasets that are compatible with some types of spatial dependence. The results state that models that merely incorporate space coordinates might be sufficient to capture space dependence. Hedonic models for Baltimore, Boston, and Toledo housing prices datasets are revisited, studied (with the new proposed procedures), and compared with standard spatial econometric methodologies. 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