Summer Term 2024
Public Economics (@University of Konstanz)
graduate/master level lecture
graduate/master level lecture
Winter Term 2023/2024
Equality of Opportunity in Germany: Evidence from the Economics of Education (@University of Konstanz)
(with Guido Schwerdt & Lennart Goldemann), undergraduate seminar in German (Chancengerechtigkeit in Deutschland: welche Befunde liefert die Bildungsökonomik?)
(with Guido Schwerdt & Lennart Goldemann), undergraduate seminar in German (Chancengerechtigkeit in Deutschland: welche Befunde liefert die Bildungsökonomik?)
Winter Term 2021/2022
New Topics in Applied Econometrics: Machine Learning, Big Data, and Other Recent Developments (@University of Munich, LMU)
(with Lukas Mergele), (undergraduate) seminar, University of Munich (LMU)
Topics covered (among others) ◆ Omitted variable bias taken seriously – Double selection and the role machine learning ◆ Recent advances in difference-in-differences designs ◆ Synthetic control ◆ The Lasso and other regression based methods for prediction ◆ Tree-Based methods for prediction ◆ Directed Acyclical Graphs (DAGs) – A new language for causality? ◆ Quantile treatment effects
(with Lukas Mergele), (undergraduate) seminar, University of Munich (LMU)
Topics covered (among others) ◆ Omitted variable bias taken seriously – Double selection and the role machine learning ◆ Recent advances in difference-in-differences designs ◆ Synthetic control ◆ The Lasso and other regression based methods for prediction ◆ Tree-Based methods for prediction ◆ Directed Acyclical Graphs (DAGs) – A new language for causality? ◆ Quantile treatment effects
Winter Term 2019/2020
Lecture on Advanced Methods in Applied Econometrics (@University of Munich, LMU)
(my own composition of Master/PhD level class in applied econometrics with applications in labor and development economics)
Introduction to Part 1: Economic Models & Econometric Analysis
◆Policy analysis ◆ Testing economic theory ◆ Forecasting and prediction ◆ Example: Estimate the parameters of a production function
Regression Basics
Linear regression model ◆ Average partial effects, and nonlinearities ◆ Bad controls ◆ Omitted variable bias ◆ Double selection of control variables ◆ Excursus I: Introduction to statistical learning ◆ Excursus II: LASSO estimator ◆ Post-LASSO double selection
Statistical Inference (Measuring Variability)
Core: ◆Parametric standard errors and their limitations ◆ Bootstrap – basic principle ◆ Bootstrap variants (b; t; residual; wild) ◆ Statistical inference with clustered data ◆ How to compute standard errors in “small g settings”? ◆ Sampling vs. design uncertainty ◆ Level of clustering? ◆ Design-based inference (Fisher (randomization) inference, exchangeability assumption) ◆
Further Topics: ◆ Multiple hypothesis testing ◆ Publication bias and p-hacking ◆ Replicability ◆ “Human” misunderstandings related to statistical inference ◆ The Cult of significance / size matters
Instrumental Variables
◆ A short review (assumption; properties; estimation: 2SLS, control functions) ◆ Selecting instruments (many instruments, LASSO-based selection) ◆ Selecting control variables in instrumental variable models (double selection with LASSO)
Measurement
◆ Measurement - The example of happiness scales ◆ Theory of scales ◆ Ordinal data and ordinal choice (e.g., Likert scales; latent variables) ◆ Ordinal data on the interval scale? ◆ Improving the interpretability of ordinal data (differential item functioning; anchoring vignettes)
Measurement Error
◆ Classical measurement error ◆ How to correct for measurement error (IV, ORIV) ◆ Measurement error and robustness checks ◆ Different types of measurement error
Languages of Causality
◆ DAGs as Language of Causality (Terminology; Do-Operator; Back-door criterion; Front-door criterion; M-bias) ◆ Potential outcomes as language of causality (Rubin causal model; formulate assumptions using potential outcomes) ◆ Economic applications (good, bad, and ugly controls; mediation and surrogates; simultaneity; robustness tests in instrumental variable models)
Introduction to Part 2: Choice, Selection, and Heterogeneity
Selection and choice ◆ Generalized Roy Model ◆ Treatment effects & selection
The Intuition of Maximum Likelihood Estimation
Concept ◆ Derivation and assumptions ◆ Estimation in Stata ◆ Statistical inference
Modelling Binary Choices
Linear Probability Model ◆ Choice and latent variables ◆ Probit models ◆ Marginal effects and interaction terms in nonlinear models
Corner Solutions & Selection
◆Understanding the difference: Truncation, censoring & corner solutions ◆ Tobit models ◆ Hurdle (two-part) models ◆ Endogenous sample selection – Heckman Selection Model
Heterogeneity & Marginal Treatment Effects
(my own composition of Master/PhD level class in applied econometrics with applications in labor and development economics)
Introduction to Part 1: Economic Models & Econometric Analysis
◆Policy analysis ◆ Testing economic theory ◆ Forecasting and prediction ◆ Example: Estimate the parameters of a production function
Regression Basics
Linear regression model ◆ Average partial effects, and nonlinearities ◆ Bad controls ◆ Omitted variable bias ◆ Double selection of control variables ◆ Excursus I: Introduction to statistical learning ◆ Excursus II: LASSO estimator ◆ Post-LASSO double selection
Statistical Inference (Measuring Variability)
Core: ◆Parametric standard errors and their limitations ◆ Bootstrap – basic principle ◆ Bootstrap variants (b; t; residual; wild) ◆ Statistical inference with clustered data ◆ How to compute standard errors in “small g settings”? ◆ Sampling vs. design uncertainty ◆ Level of clustering? ◆ Design-based inference (Fisher (randomization) inference, exchangeability assumption) ◆
Further Topics: ◆ Multiple hypothesis testing ◆ Publication bias and p-hacking ◆ Replicability ◆ “Human” misunderstandings related to statistical inference ◆ The Cult of significance / size matters
Instrumental Variables
◆ A short review (assumption; properties; estimation: 2SLS, control functions) ◆ Selecting instruments (many instruments, LASSO-based selection) ◆ Selecting control variables in instrumental variable models (double selection with LASSO)
Measurement
◆ Measurement - The example of happiness scales ◆ Theory of scales ◆ Ordinal data and ordinal choice (e.g., Likert scales; latent variables) ◆ Ordinal data on the interval scale? ◆ Improving the interpretability of ordinal data (differential item functioning; anchoring vignettes)
Measurement Error
◆ Classical measurement error ◆ How to correct for measurement error (IV, ORIV) ◆ Measurement error and robustness checks ◆ Different types of measurement error
Languages of Causality
◆ DAGs as Language of Causality (Terminology; Do-Operator; Back-door criterion; Front-door criterion; M-bias) ◆ Potential outcomes as language of causality (Rubin causal model; formulate assumptions using potential outcomes) ◆ Economic applications (good, bad, and ugly controls; mediation and surrogates; simultaneity; robustness tests in instrumental variable models)
Introduction to Part 2: Choice, Selection, and Heterogeneity
Selection and choice ◆ Generalized Roy Model ◆ Treatment effects & selection
The Intuition of Maximum Likelihood Estimation
Concept ◆ Derivation and assumptions ◆ Estimation in Stata ◆ Statistical inference
Modelling Binary Choices
Linear Probability Model ◆ Choice and latent variables ◆ Probit models ◆ Marginal effects and interaction terms in nonlinear models
Corner Solutions & Selection
◆Understanding the difference: Truncation, censoring & corner solutions ◆ Tobit models ◆ Hurdle (two-part) models ◆ Endogenous sample selection – Heckman Selection Model
Heterogeneity & Marginal Treatment Effects
Previous Years
Economics of Education (Winter Term 2018/19)
(with Larissa Zierow), Lecture (undergraduate), University of Munich (LMU)
| Syllabus |
Economics of Education (Winter Term 2016/17)
(with Francesco Cinnirella), Lecture, University of Munich (LMU)
The Development of Economic Preferences and Human Capital in Childhood
(with Philipp Lergetporer), Seminar, University of Munich (LMU)
Introduction to Decision Theory
(with Heinrich Ursprung), Lecture (development of the course, teaching tutorials), University of Konstanz
Decision Theory & Scientific Writing
(with Heinrich Ursprung), Seminar, , University of Konstanz
Microeconomics
(with Friedrich Breyer), Lecture (teaching tutorials), , University of Konstanz
(with Larissa Zierow), Lecture (undergraduate), University of Munich (LMU)
| Syllabus |
Economics of Education (Winter Term 2016/17)
(with Francesco Cinnirella), Lecture, University of Munich (LMU)
The Development of Economic Preferences and Human Capital in Childhood
(with Philipp Lergetporer), Seminar, University of Munich (LMU)
Introduction to Decision Theory
(with Heinrich Ursprung), Lecture (development of the course, teaching tutorials), University of Konstanz
Decision Theory & Scientific Writing
(with Heinrich Ursprung), Seminar, , University of Konstanz
Microeconomics
(with Friedrich Breyer), Lecture (teaching tutorials), , University of Konstanz
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