First semester

This course explores the consequences and solutions to violations of key assumptions in linear regression models within econometrics. Students will examine issues such as heteroscedasticity, multicollinearity, autocorrelation, and endogeneity, and how they affect the reliability of regression estimates. The course covers diagnostic tools for detecting assumption violations, including residual analysis and specification tests. Emphasis is placed on addressing these issues through methods like robust standard errors, instrumental variables, and generalized least squares. Students will apply these techniques to real-world data, learning how to correct for violations and interpret results accurately. By the end of the course, students will be equipped to conduct reliable regression analysis even in the presence of assumption violations.