Introduction to Econometrics: Theory and practice

Econometrics theory, derivations, proofs, hypothesis testing, diagnostic tests


The course Introduction to Econometrics: Theory and Practice is designed to equip students with the essential tools and knowledge required to analyze economic data, test economic theories, and make informed decisions in the real world. This course bridges the gap between economic theory and empirical analysis, offering a balanced blend of theoretical concepts and hands-on practical application. Throughout the course, students will delve into the core principles of econometrics, learning how to formulate and estimate econometric models, assess their validity, and draw meaningful conclusions. Topics covered include simple and multiple regression analysis, assumptions of classical linear regression models, hypothesis testing, and diagnostic tests for model validation. Students will gain a deep understanding of regression analysis, assumptions of Ordinary Least Squares (OLS), and how to derive OLS parameters and proofs of the Best Linear Unbiased Estimators (BLUE) properties. The course places a strong emphasis on understanding the underlying assumptions and limitations of econometric models, ensuring that students can identify and address common issues such as multicollinearity, heteroscedasticity, autocorrelation, and endogeneity. By the end of this course, students will not only have a solid theoretical foundation in econometrics but also practical skills to address complex economic questions and contribute to evidence-based decision-making in various fields such as economics, finance, and public policy.

Who this course is for:

  • The course “Introduction to Econometrics: Theory and Practice” is typically designed for students who have a basic understanding of economics and a strong interest in quantitative analysis. It serves as an entry-level course that introduces students to the field of econometrics, which involves the application of statistical and mathematical methods to economic data.

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