Econ5121ABC

Econ5121 ABC 2024

This course will be taught by three instructors. I will cover the following topics.

Lecture 9: Maximum likelihood
- Maximum likelihood estimation (MLE) is a fundamental method in econometrics for estimating the parameters of a model. This technique involves choosing the parameter values that maximize the likelihood function. This lecture will explore the theory behind MLE.
Lecture 10: Limited dependent variables
- Limited dependent variables are outcomes that do not vary continuously but are bounded or discrete, such as binary, ordinal, or count data. This lecture will cover models such as logistic regression for binary data and Poisson regression for count data, emphasizing their assumptions, estimation, and applications.
Lecture 11: Causal inference
- Causal inference is a central goal in econometric analysis, aiming to determine the effect of one variable on another. This lecture will dive into the methods used to infer causality, such as randomized experiments, natural experiments, and regression discontinuity designs. We will discuss the assumptions under which these methods yield causal conclusions.
Lecture 12: Panel data
- Panel data, or longitudinal data, involves observations of multiple time periods for the same firms, individuals, or countries. This lecture will introduce the basics of panel data analysis, including fixed effects and random effects models, which help to control for unobserved heterogeneity. We will discuss how these models are estimated, their advantages and limitations.