Advanced Econometrics

How does increasing the Minimum Wage affect youth unemployment? Is rent control an effective policy? Did some drug really improve patients' health?

All of these causal questions require quantitative techniques to measure treatment effects.

In this repository several statistical properties are derived for Advanced Data Analytics, specifically for Causal Inference. Additionally, selected solutions to Fumio Hayashi's Econometrics are provided.

Causal Inference Framework

It is encouraged to follow the documents in the presented order so as to fully grasp Causal Inference methods.

In Finite Sample Theory, restrictive assumptions on the sample $(\textbf{y}, \textbf{X})$ are made.

Then, in Large Sample Theory, said assumptions are relaxed and made on the Data Generating Process (DGP).

Finally, correlation with the error term is tackled with Method of Moments and General Method of Moments estimation.

• Finite Sample Causal Inference.

• Large Sample Causal Inference.

• GMM Causal Inference.

Python implementations

Algorithms, estimation procedures and Statistical Inference tests coded in Python programming language.

• Python code.

Hayashi's Econometrics

Selected solutions of graduate-level Quantitative Economics textbook.

• Solutions to Econometrics.

Project started in 03/2022 (ongoing).