Course Introduction

Advanced Econometrics

Vladislav Morozov

Content and Motivation

About Me

Instructor: JProf. Dr. Vladislav Morozov

  • Institute of Finance and Statistics
  • Email:

    morozov@uni-bonn.de

  • I work on practical statistical methods with a focus on unobserved heterogeneity

This Course: Moving Beyond Basics

Remind: Basic Econometrics

Basic econometrics was mostly about the simple model \[ Y_{i} = \beta_1 + \beta_2 X_i^{(2)} + \dots + \beta_k X_i^{(k)} + U_i \] where

  • Sample — a cross-section: one observation per unit \(i\)
  • The linear model was causal (\(\E[U_i|X_i]=0\) or an instrument was available)

What is Advanced Econometrics?

In short, advancing in every aspect:

  • Working with more data types: time series and panel data
  • More flexible and general models: nonlinearities
  • Introducing prediction
  • Causal perspectives with new data types and nonlinearities
  • More explanations about theory: why things work the way they do

Main Course Blocks

  1. More on linear regression and linear IV
  2. Panel data in causal settings
  3. Introduction to forecasting
  4. Time series
  5. Parametric nonlinear models

May cover more if time allows

Should You Attend This Course?

Yes, if

  • If you want to work with data at all:
    • Applied micro- or macroeconomics
    • Data science-adjacent careers
    • Just for fun
  • If you want to do a master’s degree in economics, finance, or data science

Course Logistics

Organization and Evaluations

Meeting Times

Lectures twice a week:

  • Wednesdays, Room 0.042, 8:30-10:00
  • Fridays, Lecture Hall N, 8:30-10:00


Any modifications will be announced on eCampus and in class

Course Format

Lecture-based course with two kinds of lectures:

  • Blended lectures on theory and empirical illustrations
  • Exercise sessions every few lectures based on problem sets

Active questions encouraged! Also feel free to approach me after/before class or use office hours

Evaluation

Course grade is based on a closed book written exam


  • Date to be announced by the Examination Office
  • Closed book
  • Preparation: lecture materials and problem sets

Course Materials

Materials

This course will be based on lectures and slides


Textbooks:

  • Course draws on several, none covers everything
  • I gave preference to books available online from authors or through the library
  • Each set of slides — specific references

General Textbooks and Causal Perspectives

Wooldridge (2020)

Cunningham (2021)

Huntington-Klein (2025)

Resource on Time Series and Prediction

Brockwell and Davis (2016)

James et al. (2023)

Wooldridge (2020)

More Advanced Books

Hansen (2022)

Hayashi (2000)

drawing

Hastie, Tibshirani, and Friedman (2009)

Some Materials on Python

Zingaro (2021)

Lau (2023)

Heiss and Brunner (2024)

Where Does This Course Fit?

Other Complementary Courses

Course complements other data-oriented courses

Some other courses:

  • Applied Microeconometrics (JProf. Dr. Aapo Stenhammar)
  • Causal Inference (JProf. Dr. Claudia Noack)
  • Nonparametric Statistics (Dr. Dennis Schroers)
  • Computer-Aided Statistical Analysis (Prof. Dr. Dominik Liebl)

Low overlap — each class tries to maximize value added

References

Brockwell, Peter J., and Richard A. Davis. 2016. Introduction to Time Series and Forecasting. Springer Texts in Statistics. Springer International Publishing.
Cunningham, Scott. 2021. Causal Inference: The Mixtape. Yale University Press. https://doi.org/10.2307/j.ctv1c29t27.
Hansen, Bruce. 2022. Econometrics. Princeton_University_Press.
Hastie, Trevor, Robert Tibshirani, and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer Series in Statistics. New York, NY: Springer.
Hayashi, Fumio. 2000. Econometrics. New Jersey: Princeton University Press.
Heiss, Florian, and Daniel Brunner. 2024. Using Python for Introductory Econometrics. 2nd edition. New York: Independently Published.
Huntington-Klein, Nick. 2025. The Effect: An Introduction to Research Design and Causality. S.l.: Chapman and Hall/CRC.
James, Gareth, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan E. Taylor. 2023. An Introduction to Statistical Learning: With Applications in Python. Springer Texts in Statistics. Cham: Springer.
Lau, Sam. 2023. Learning Data Science. 1st ed. Sebastopol: O’Reilly Media, Incorporated.
Wooldridge, Jeffrey M. 2020. Introductory Econometrics: A Modern Approach. Seventh edition. Boston, MA: Cengage.
Zingaro, Daniel. 2021. Learn to Code by Solving Problems: A Python Programming Primer. New York: No Starch Press.