What you'll learn
Students will grasp the fundamental concepts of econometrics, including the data types, assumptions in econometric models and properties.
Estimate basic econometric models e.g simple linear regression and multiple linear regression and interpret the results.
Diagnosing violations of key assumptions e.g normality, multicollinearity, heteroscedasticity, autocorrelation endogeneity etc.
Conducting hypothesis tests for individual coefficients and overall model significance.
Requirements
You will learn everything you need to know in this course; however, having a foundation in basic mathematics and statistics will be advantageous
Description
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.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 course outline and overview
Section 2: Module 1: Foundations of Econometrics
Lecture 3 Significance of econometrics and data types
Lecture 4 The Econometric Process and Model Building
Section 3: Module 2: Simple Linear Regression
Lecture 5 Understanding Regression Analysis and derivation
Lecture 6 The alternate formula of OLS estimators
Lecture 7 Understanding the assumptions of Classical Linear Regression Model
Lecture 8 BLUE property of OLS estimators; Concept and Proof
Lecture 9 Derivation of the variance of OLS estimators
Lecture 10 R-Square; concept and alternative formulas
Lecture 11 Hypothesis Testing in Simple Linear Regression
Lecture 12 Testing the normality of errors
Section 4: Module 3: Multiple Linear Regression
Lecture 13 Multiple Linear Regression Model and derivation of OLS estimators
Lecture 14 Derivation of variance
Lecture 15 Hypothesis Testing and Inference in Multiple Regression
Lecture 16 F-test
Lecture 17 Chow test
Section 5: Module 4: Violation of assumptions, consequences, detection and remedies
Lecture 18 multicollinearity and its causes
Lecture 19 Consequences of multicollinearity
Lecture 20 Detection of multicollinearity
Lecture 21 Remedial Measures
Section 6: Autocorrelation
Lecture 22 Types and causes of autocorrelation
Lecture 23 Consequences of autocorrelation
Lecture 24 Detection of Autocorrelation
Lecture 25 Remedial Measures
Section 7: Heteroscadasticity
Lecture 26 Causes of Heteroscedasticity
Lecture 27 Consequences of heteroscedasticity
Lecture 28 Detection of heteroscedasticity
Lecture 29 Remedial measures
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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