Introduction To Econometrics: Theory And Practice

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

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.