Lehrangebot im Wintersemester 2025/26

Content

Today, quantitative analysis is the primary tool of economists. The course will introduce you to the statistical software – Stata – widely used in empirical research. Participants will master data collection, building datasets, data diagnostics, regression analysis, and production of tabulated and graphical output. The course's ultimate goal is to provide the students with the knowledge and programming skills sufficient to undertake empirical research on their own (e.g., for writing their master thesis). 

The lecture part will introduce primary commands with practical examples. The tutorial part will focus on the coding exercises employing datasets from the existing empirical papers on the political economy and development economics. Both parts of the course require diligent attendance and will be followed by the exam. 

 Requirements

The course is restricted to 16 participants. The main requirement is a good knowledge of econometrics. If you are interested in participating, please send an e-mail to nikita.zakharov@vwl.uni-freiburg.de. Please attach your transcript or records so that we can see which econometrics courses you have taken so far. The deadline for application is October 1. Later enrolment is possible as long as places are available.

You do not need a STATA-license to participate as you will have access to STATA through the university.

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Content

The updated course covers empirical labor economics, modern econometrics, and modern business analytics. The main textbooks are Taddy et al. (2023) and Huber (2023). R scripts are used for each session. Students should sign in at posit.cloud to get access to RStudio. Each session will be recorded via Zoom.

Complementary YouTube-Videos are available on my channel (https://www.youtube.com/chan-nel/UCi5oODsxMKzuMiI9PFp3CSw).

Topics covered are:

• Regression in R
• Causal Inference with Experiments (RCT, RDD, IV)
• ­Causal Inference with Controls (Matching, DiD)
• Labor Market and Education (Mincer equation)
• Simulation-based Inference (SBI): Bootstrapping (Resampling), Shuffling
• Introduction to Causal Machine Learning (LASSO, Random Forest)

 Lecture

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