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2165-Microeconometrics-2425_S2
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Courses
2024/25-Masters
2Sem
2165-2425_S2
Identification in linear models (cross-section data)
APPLICATION: The Colonial Origins of Comparative Development: An Empirical Investigation (Acemoglu et al.)
APPLICATION: The Colonial Origins of Comparative Development: An Empirical Investigation (Acemoglu et al.)
Click
The Colonial Origins of Comparative Development (Acemoglu et al.).pdf
link to view the file.
◄ Linear model with cross section data (updated)
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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
VOLUNTARY READING Identification Problems in the Social Sciences and Everyday Life (Manski)
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: 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)
APPLICATION: Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size (Angrist and Evans) ►
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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