Última actualización: 05/07/2019


Curso Académico: 2019/2020

Economía Aplicada I
(16865)
Titulación: Máster Universitario en Análisis Económico (68)
Escuela de Economía y Ciencia Política


Coordinador/a: ALONSO BORREGO, CESAR

Departamento asignado a la asignatura: Departamento de Economía

Tipo: Obligatoria
Créditos: 9.0 ECTS

Curso:
Cuatrimestre:




Materias que se recomienda haber superado
Graduate courses in Statistics, Econometrics I and Econometrics II (Master in Economic Analysis)
Competencias que adquiere el estudiante y resultados del aprendizaje.
The goal of this course is to link econometric methods for estimation of causal effects to data. We will cover a number of theoretical topics that are important in applied research in labor economics, health economics, industrial organization and related fields. The course will be organized in lectures to provide the economic framework and the econometric issues for each topic. The lectures will be complemented with problem sets, that include both theoretical and empirical exercises. Students ought to handle the Stata program on their own and read related papers.
Descripción de contenidos: Programa
1. Empirical strategies for identification of causal effects 1.1. Aims and methods of empirical research 1.2. Microeconomic data structures 1.3. Causal relationships of interest 1.4. The identification problem: potential outcomes and causality 2. Social experiments 2.1. Advantages of randomized experiments: The independence condition. 2.2. Internal and external validity 2.3. Examples 3. Selection on observables 3.1. Identification with observational data 3.2. Conditional independence 3.3. Conditional mean-independence 3.4. Regression and causality 4. Matching 4.1. Introduction 4.2. Matching methods and assumptions 4.3. Propensity score 4.4. Relation with regression 5. Identification using external information 5.1. Natural experiments and instrumental variables (IV) 5.2. Identification using IV. The Wald estimator 5.3. Local average treatment effects (LATE) 5.4. Control function approach 6. Regression Discontinuity (RD) designs 6.1. Discontinuities in assignment rules 6.2. Sharp and fuzzy RD designs 7. Differences in Differences (DD) 7.1. Natural experiments and DD 7.2. The fundamental identification assumption 7.3. Differences in differences in differences (DDD) 7.4. Synthetic control methods 7.5. DD with panel data 8. Quantile methods 8.1. Unconditional and conditional quantiles 8.2. Quantile regression (QR). Interpretation 8.3. Extensions 9. Structural estimation 9.1. Policy parameters 9.2. Computational problems 9.3. Methods of estimation 9.4. Applications
Sistema de evaluación
  • Peso porcentual del Examen Final 45
  • Peso porcentual del resto de la evaluación 55
Bibliografía básica
  • A. Colin Cameron & Pravin K. Trivedi . Microeconometrics: Methods and Applications. Cambridge University Press. 2005
  • Jeffrey M. Wooldridge . Econometric Analysis of Cross Section and Panel Data. MIT Press. 2010
  • Joshua D. Angrist & Jörn-Steffen Pischke. Mostly Harmless Econometrics. An Empiricist's Companion. Princeton University Press. 2009
  • Pravin K. Trivedi & A. Colin Cameron . Microeconometrics Using Stata, Revised Edition. Stata Press. 2010
  • Scott Cunningham. Causal Inference: The mixtape. tufte-latex.googlecode.com. 2018

El programa de la asignatura y la planificación semanal podrían sufrir alguna variación por causa de fuerza mayor debidamente justificada o por eventos académicos comunicados con antelación.