QMUL - The Classical Linear Regression Model
- Offered byCoursera
The Classical Linear Regression Model at Coursera Overview
Duration | 22 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
The Classical Linear Regression Model at Coursera Highlights
- Flexible deadlines in accordance to your schedule.
- Earn a Certificate upon completion
The Classical Linear Regression Model at Coursera Course details
- In this course, you will discover the type of questions that econometrics can answer, and the different types of data you might use: time series, cross-sectional, and longitudinal data.
- During the course you will:
- ' Learn to use the Classical Linear Regression Model (CLRM) as well as the Ordinary Least Squares (OLS) estimator, as you discuss the assumptions needed for the OLS to deliver true regression parameters.
- ' Look at cases with only one independent variable for one dependent variable, before progressing to regression analysis by generalising the bivariate model to multiple regression.
- ' Explore different model-building philosophies, with particular focus on the general-to-specific approach, and learn how to use goodness-of-fit statistics as the measures of 'how well your model explains variations in the dependent variable'.
- Throughout this course, you will see examples to help clarify which kind of relationship is of interest, and how we can interpret it. You will also have the opportunity to apply your learning to estimating the Capital Asset Pricing Model using real data with R.
- The course is for beginners, so little prior knowledge is required, but you will benefit from an ability to graph two variables in the xy framework, an understanding of basic algebra and taking derivatives. Knowledge of matrix algebra is not a requirement but will also provide you with an advantage.
- By the end of this course, you will be able to:
- ' Describe the problems that econometrics can help addressing and the type of data that should be used
- ' Explain why some hypotheses are needed for the approach to produce an estimate
- ' Calculate the coefficients of interest in the classical linear regression model
- ' Interpret the estimated parameters and goodness of fit statistics
- ' Estimate single and multiple linear regression models with R.
The Classical Linear Regression Model at Coursera Curriculum
Aims and Uses of Econometrics
Welcome to Econometrics for Economists and Finance Practitioners
Types of Data
The Problem
A Basic Problem to Solve
Welcome to Coursera
Welcome to Queen Mary University of London
Overview
Weekly Content
Discussion Forums
Grades
Notes
Top Tips
Workload
Mastery Learning
Unfacilitated
Academic Integrity
Assessment
Peer Review
Quizzes
Course Projects
Labs and Programming Assignments
Getting Started
Econometrics and Decision Making
Cross-Sectional Data
Time Series Data
High Frequency Data
Panel Data
Assumptions
Solving the Problem
Setting Your Goals
Check Understanding: Types of Data
Solving the OLS Problem
Check Understanding: Simple Linear Regression Model
The Classical Linear Regression Model
An Introduction to the Classical Linear Regression Model
Setting the Scene
Solution Parameters
Solution Variance
The Problem
Multiple Regression and the Constant Term
Linearity
Full Rank
Regression Model
Spherical Errors
Non-Stochastic Regressors
Looking for the Optimal Beta
Working Out the Variance
Simple Regression
Working Out the Solution
Changes in the Sample
Estimating the Variance
Understanding The Classical Linear Regression Model
Interpretation of the Ordinary Least Squares Parameters
The OLS Parameters
Goodness of The Fit - The R2
Goodness of Fit - The Adjusted R2
A Policy Example
Interpreting the Parameters
Interpreting the Variance
The R2
Problems with the R2
The Adjusted R2
Explaining Bus Use
Build a Robust Model
Problems with the Adjusted R2
Interpreting the Ordinary Least Squares Parameters
Capital Asset Pricing Model
An Introduction to the Capital Asset Pricing Model (CAPM)
Long and Short Memory
The CAPM
Estimation of the OLS Parameters The CAPM
Interpreting the OLS Parameters
The Theory of the CAPM
Congratulations
From Theory to Practice
Implications of the Fama-French Model
Analysing the Fama-French Model
Interpreting the Intercept and the Slopes
The Classical Linear Regression Model at Coursera Admission Process
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