KECD 2235 - ECONOMETRICS 2 : Estimation and inference

Course description:


The course follows up on the course taught in the first semester on probability and statistics.

It introduces advanced econometric techniques used in empirical economics with cross-sectional data.

The course discusses identification and inference in linear, nonlinear, and semiparametric settings, under standard and nonstandard conditions.

Facundo ARGANARAZ,Nikiforos ZAMPETAKIS

Cours magistral et conférences

English

Additional information: (outline, a few bibliographical references)



Outline


1. Inference in linear models, identification and causal effects, OLS estimates, measurement error, control variables, testing.

2. Inference with structural linear equations, Two-stage least square estimates, specification tests (endogeneity, functional form, heteroskedasticity).

3. Inference with a multivariate system of linear equations based on OLS, GLS, and FLS; Seemingly unrelated systems of equations; the generalized method of moments (2SLS and 3SLS), optimal instruments.

4. Inference with nonlinear models: Nonlinear regression, maximum likelihood, quantile regression, minimum distance, M and Z estimators; Wald, LM, and LR tests for general models; Numerical optimization methods; GMM under general settings, asymptotic theory.

5. Causal Inference with Machine Learning.

Prerequisites


Students should have a robust knowledge of probability and statistical theory, covered in Econometrics 1. Some knowledge of R might be ideal.

Spring 2025-2026

Assessment


Final Exam (60%) + Midterm Exam and Problem Sets (40%).

Organization


This course is organized through lectures, practical classes, problem sets, office hours, and individual and collaborative student work.