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2165-Microeconometrics-2425_S2
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Courses
2024/25-Masters
2Sem
2165-2425_S2
General microeconometrics approach
VOLUNTARY READING Identification Problems in the Social Sciences and Everyday Life (Manski)
VOLUNTARY READING Identification Problems in the Social Sciences and Everyday Life (Manski)
Click
Identification Problems in the Social Sciences and Everyday Life (Manski).pdf
link to view the file.
◄ The general microeconometrics approach
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Course Syllabus
Announcements
SAMPLE exam
Sample questions
Last year final exam (with solutions)
Final Grades
Final Exam - Solution
Q&A for Problem Sets - Add here questions or doubts you have about the problem sets
Problem set 1 - due March 3rd by 11am
Problem set 1 - Datasets
Problem set 1 - Solution
Problem set 2 - due Apr 7th by 11am
Problem set 2 - Datasets
Problem set 2 - Solution
Problem set 3 - due May 5h by 11am
Problem set 3 - Datasets
Problem set 3 - Solution
Group assignment submission - due May 11th (one submission per group is enough - indicate all names in the submitted file)
Group assignment
Submit group members - max 4 students per group
Groups for group assignment
The general microeconometrics approach
DATA: Dataset for replication of empirical part (STATA format)
DATA: Dataset for replication of empirical part (text delimited format)
Linear model with cross section data (updated)
APPLICATION: The Colonial Origins of Comparative Development: An Empirical Investigation (Acemoglu et al.)
APPLICATION: Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size (Angrist and Evans)
APPLICATION: Does compulsory school attendance affect schooling and earnings (Angrist and Krueger)
DATA: Dataset for replication of empirical part on OLS (Stata format)
DATA: Dataset for replication of empirical part on OLS (text delimited format)
DATA: Dataset for replication of empirical part on IV (STATA format)
DATA: Dataset for replication of empirical part on IV (text delimited format)
Counterfactuals
DATA: Dataset for replication of empirical part (STATA format)
DATA: Dataset for replication of empirical part (text format)
CODE: STATA code for replicating empirical part
APPLICATION: Evaluating the econometric evaluations of training programs with experimental data (Lalonde)
APPLICATION: Incentive effects of social assistance: A regression discontinuity approach (Lemieux and Milligan)
ADDITIONAL READING ON LATE: Identification and Estimation of Local Average Treatment Effects (Imbens and Angrist)
Panel data models
APPLICATION: The costs of low birth weight (Almond et al.)
APPLICATION: Minimum wages and employment - A case study of the fast-food industry in New Jersey and Pennsylvania (Card and Krueger)
DATA: Dataset for replication of empirical part (STATA format)
DATA: Dataset for replication of empirical part (text delimited format)
Introduction to non-linear models
PROOF FOR VOLUNTARY EXERCISE: estimation of parameters of a normal distribution using MLE
Binary and discrete outcomes
APPLICATION: The sensitivity of an empirical model of married women's hours of work to economic and statistical assumptions (Mroz)
DATA: Dataset for replication of empirical part (text delimited format)
DATA: Dataset for replication of empirical part (STATA format)
Data censoring and corner solution outcomes
DATA: Dataset for replication of empirical part (STATA format)
DATA: Dataset for replication of empirical part (text delimited format)
Missing data and selection models
DATA: Dataset for replication of empirical part (STATA format)
DATA: Dataset for replication of empirical part (text delimited format)
DATA: Dataset for replication of empirical part (STATA format) ►
2165-2425_S2
General
Problem sets
Group assignment
General microeconometrics approach
Identification in linear models (cross-section data)
Counterfactuals: from ATE to LATE
Identification in linear models with panel data
Non-linear models and MLE
Binary and discrete outcomes
Data censoring and corner solution outcomes
Missing data and selection models
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