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    Introduction To Econometrics: Theory And Practice

    Posted By: ELK1nG
    Introduction To Econometrics: Theory And Practice

    Introduction To Econometrics: Theory And Practice
    Published 9/2023
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 1.14 GB | Duration: 3h 36m

    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.