Econometrics.

Catalog description

This course has two main parts. The first part introduces students to the principles of econometric analysis and their practical applications. Topics include simple and multiple regression models, interval estimation, hypothesis testing, dummy variables, model specification, panel and experimental data analysis. Students will also gain hands-on experience using Generative Artificial Intelligence (Gen AI) to estimate, test, and forecast empirical relationships. In the second part of the course, students will apply their knowledge and Gen AI skills to a collaborative empirical project. Working in teams, students will choose a research topic, formulate questions, gather and clean data, conduct regression analysis, and interpret findings. Finally, students will evaluate the project’s limitations and communicate results through both a written report and an oral presentation. Prerequisite: ( ECON 321 or MATH 263 or MATH 363 or MATH 163 (AP Statistics credit)), and ( ECON 326 or ECON 325 ).