Understanding econometric modeling

optional courses

The course aims to extending the econometric knowledge much above the basic courses, but does not concentrate on the mathematical side of econometrics. On the contrary, it aims to visualize econometrics using simple simulation-based methods.

Such simulations can help to understand the conditions when the words “causes”, “influences” can be used, which is improvement from correlational to causal analysis.

Attending this course is reasonable after finishing basic Econometrics course. Finishing Advanced Econometrics can be somehow helpful, but is not a required milestone to start this course.

This course requires classroom activity from students. Activity during classes is a basis for the grading process.

First, methodology:

With just a few lines of R or Python code one can create simplistic econometric simulation. Programing skills required for such a task are limited to absolute minimum. Such simplistic simulation however, let us to answer the question if the model is correct or not. Usage such simulations can be extended to answering many methodological econometric questions, which are hard, or impossible to answer using mathematical derivations.

This way, in much simpler manner, one can get answers for much harder questions. Answers which can be both correct, as well as surprising in the context of econometric theory and practice.

Second, subject:

The main problem of econometric modeling can not be found among ideas known from basic courses: nonlinearity, autocorrelation, heteroskedasticity, or non-normality of model residuals. The fundamental problem of econometrics is variable selection, which will allow for the interpretation of the estimated parameter. For the last two decades the whole scientific branch dedicated to this topic emerged. It is called Causal Inference.

During this course, according to ability and willingness of the group, consecutive fundamental Causal Inference topics will be discussed. These topics gathered together can give clues which variables should be included in the model and why – and as a consequence allow for using words “cause” and “effect” correctly, instead of overinterpreting partial correlation as causation. It is huge shift of the level of the econometric modelling.

During the course, according to ability and willingness of the group, we will discuss following topics:

Fundamentals of Causal Inference:

• Confounder

• Mediator

• Collider

• M-Bias

• Butterfly Bias

Extension of identification problems:

• Attenuation Bias

• Suppressor

• Reversed Causality

• Sample Selection

Solutions for previously known problems:

• Experiment

• Instrumental Variable

• Front Door Criterion

• Regression Discontinuity Design

• Linear Regression

• Dynamic modelling

Basic literature:

Hernan, M.A., & Robins, J.M. Causal Inference: What If (1st ed.). CRC Press, 2023.

Lewbel, Arthur. "The identification zoo: Meanings of identification in econometrics." Journal of Economic Literature 57, no. 4 (2019): 835-903.

Imbens, Guido. Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics. No. w26104. National Bureau of Economic Research, 2019.

Pearl, Judea, and Dana Mackenzie. The book of why: the new science of cause and effect. Basic Books, 2018.

Elwert, Felix, and Christopher Winship. "Endogenous selection bias: The problem of conditioning on a collider variable." Annual review of sociology 40 (2014): 31-53.

As well as supplementary materials for selected topics.

KNOWLEDGE

• Knows fundamental econometrics and Causal Inference problems

• Knows simulation-based approach to assessment of correctness of econometric models

• Knows programming basics which allow for numerical experiments

ABILITIES

• Can independently and critically analyse econometric results

• Can use different datasets for own research

SOCIAL COMPETENCE

• Is critical towards econometric and statistical results in social sciences, can explain economic and social phenomena in terms of causality, learns to think, communicate and write in a logical and consistent manner.

The grade is based on the classroom activity.