Introduction to Econometrics: Theory and practice

Introduction to Econometrics: Theory and practice, available at $19.99, has an average rating of 4.14, with 29 lectures, 2 quizzes, based on 42 reviews, and has 1259 subscribers.

You will learn about 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. This course is ideal for individuals who are 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. It is particularly useful 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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Summary

Title: Introduction to Econometrics: Theory and practice

Price: $19.99

Average Rating: 4.14

Number of Lectures: 29

Number of Quizzes: 2

Number of Published Lectures: 29

Number of Published Quizzes: 2

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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.

Target Audiences

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.

The course Introduction to Econometrics: Theory and Practiceis 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: course outline and overview

Chapter 2: Module 1: Foundations of Econometrics

Lecture 1: Significance of econometrics and data types

Lecture 2: The Econometric Process and Model Building

Chapter 3: Module 2: Simple Linear Regression

Lecture 1: Understanding Regression Analysis and derivation

Lecture 2: The alternate formula of OLS estimators

Lecture 3: Understanding the assumptions of Classical Linear Regression Model

Lecture 4: BLUE property of OLS estimators; Concept and Proof

Lecture 5: Derivation of the variance of OLS estimators

Lecture 6: R-Square; concept and alternative formulas

Lecture 7: Hypothesis Testing in Simple Linear Regression

Lecture 8: Testing the normality of errors

Chapter 4: Module 3: Multiple Linear Regression

Lecture 1: Multiple Linear Regression Model and derivation of OLS estimators

Lecture 2: Derivation of variance

Lecture 3: Hypothesis Testing and Inference in Multiple Regression

Lecture 4: F-test

Lecture 5: Chow test

Chapter 5: Module 4: Violation of assumptions, consequences, detection and remedies

Lecture 1: multicollinearity and its causes

Lecture 2: Consequences of multicollinearity

Lecture 3: Detection of multicollinearity

Lecture 4: Remedial Measures

Chapter 6: Autocorrelation

Lecture 1: Types and causes of autocorrelation

Lecture 2: Consequences of autocorrelation

Lecture 3: Detection of Autocorrelation

Lecture 4: Remedial Measures

Chapter 7: Heteroscadasticity

Lecture 1: Causes of Heteroscedasticity

Lecture 2: Consequences of heteroscedasticity

Lecture 3: Detection of heteroscedasticity

Lecture 4: Remedial measures

Instructors

Zakia Batool
Instructor at Udemy

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1 stars: 0 votes
2 stars: 3 votes
3 stars: 5 votes
4 stars: 7 votes
5 stars: 27 votes

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