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ECO-5106 : Topics in econometrics : Panel data and discrete choice

Niveau M2

Discipline(s) Economie

ECTS 4.00

Période 1e semestre

Localisation Site Descartes

Année 2019-2020

 Public externe (ouverts aux auditeurs de cours)
 

Créneau
Lundi Matin
Mardi Matin
Mercredi Matin
Jeudi Matin
Vendredi Matin
Vendredi Après-midi
Horaires du cours
Vendredi 8/11/2019 8H30-11H30; Vendredi 22/11/2019 13H-16H; Jeudi 28/11/2019 8H30-11H30; Jeudi 5/12/2019 8H30-11H30; Lundi 13/01/2020 8H30-11H30; Mardi 14/01/2020 8H30-11H30; Mercredi 15/01/2020 8H30-11H30; Jeudi 16/01/2020 8H30-11H30.
Objectif du cours

This course introduces the students to advanced econometric methods for the analysis of cross-sectional and panel micro-data. The course explores different techniques that are used in empirical research in economics, as well as policy evaluation tools.

The objective of this course is to first gain a good understanding of the theory behind the models discussed, and then a have broad perspective of how these models are used in practice. 

This course is structured in two blocks:

  • PART 1: PANEL DATA
    • Introduction
    • Pooled models
    • Random effects models
    • Fixed effects models
    • Differences-in-differences Estimator
    • Dynamic models

 

  • PART 2: DICRETE-TIME MODELS
    • Introduction
    • Maximum Likelihood Estimation: A reminder
    • Binary models
    • Multinomial models
    • Sample selection models

 

Students should be familiar with the most common statistical methods in econometrics, e.g. linear estimators such as OLS and GLS. 

Lecturer : Lavinia Piemontese and Jean-Paul Renne.

Students will be evaluated on the basis of paper presentations (30%) and a final exam (70%)

Cameron and Trivedi, (2005) Microeconometrics: Methods and Applications, Cambridge University Press.

Angrist and Pischke, (2008) Mostly Harmless econometrics: An Empiricist’s Companion, Princeton University Press

Papers that will be discussed in class (subject to change):

Arellano and Bond (1991), Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, Review of Economic Studies, 58, 277-297.

Ashenfelter and Krueger (1994), Estimates of the Economic Return to Schooling from a New Sample of Twins. The American Economic Review, vol. 84, no. 5, 1994, pp. 1157–1173. 

Card and Krueger (1994), Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania, American Economic Review, vol. 84, issue 4, 772-93.

Freeman (1984), Longitudinal Analyses of the Effects of Trade Unions. Journal of Labor Economics Vol. 2, No. 1 (Jan., 1984), pp. 1-26.

Guryan (2004), Desegregation and Black Dropout Rates, American Economic Review, 94 (4): 919-943.

Heckman, J. J. (1979), Sample Selection as a Specification Error, Econometrica, 47, 153-161.

Herriges, J. A., and C. L. Kling (1999), Nonlinear Income Effects in Random Utility Models, Review of Economics and Statistics, 81, 62-72.

Pischke (2007), The Impact of Length of the School Year on Student Performance and Earnings: Evidence From the German Short School Years, The Economic Journal, 117: 1216-1242. 

Modifié le :
10/07/2019 15:19:41