QMUL - Hypotheses Testing in Econometrics
- Offered byCoursera
Hypotheses Testing in Econometrics at Coursera Overview
Duration | 27 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Hypotheses Testing in Econometrics at Coursera Highlights
- Flexible deadlines in accordance to your schedule.
- Earn a Certificate upon completion
Hypotheses Testing in Econometrics at Coursera Course details
- In this course, you will learn why it is rational to use the parameters recovered under the Classical Linear Regression Model for hypothesis testing in uncertain contexts. You will:
- Develop your knowledge of the statistical properties of the OLS estimator as you see whether key assumptions work.
- Learn that the OLS estimator has some desirable statistical properties, which are the basis of an approach for hypothesis testing to aid rational decision making.
- Examine the concept of null hypothesis and alternative hypothesis, before exploring a statistic and a distribution under the null hypothesis, as well as a rule for deciding which hypothesis is more likely to hold true.
- Discover what happens to the decision-making framework if some assumptions of the CLRM are violated, as you explore diagnostic testing.
- Learn the steps involved to detect violations, the consequences upon the OLS estimator, and the techniques that must be adopted to address these problems.
- By the end of this course, you will be able to:
- Explain what hypothesis testing is
- Explain why the OLS is a rational approach to hypothesis testing
- Perform hypothesis testing for single and multiple hypothesis
- Explain the idea of diagnostic testing
- Perform hypothesis testing for single and multiple hypothesis with R
- Identify and resolve problems raised by identification of parameters.
Hypotheses Testing in Econometrics at Coursera Curriculum
Properties of the OLS Approach
Welcome to Hypotheses Testing in Econometrics
Properties of the OLS Estimator
Presentation of Linearity
Unbiasedness
Efficiency
Consistency
Understanding Linearity of the OLS Estimator
Understanding Unbiasedness
Understanding Efficiency
Understanding Consistency
Linearity
Check Your Understanding of Unbiasedness
Check Your Understanding of Efficiency
Check Your Understanding of Consistency
Knowledge Check: Properties of the OLS Approach
Hypothesis Testing
Hypothesis Testing
The t-Test
The F-Test
Type I and Type II Errors
Using Hypothesis Testing
Exploring the Test of Significance
Example of the t-Test
Test Joint Hypothesis
An Example
Types of Errors
Building a Hypothesis
Interpreting t-Tests
Conditions for the f-Test
Differences between t and F-Tests
Non-Nested Models
Check Your Understanding of Hypothesis Testing
Knowledge Check: Hypothesis Testing
Diagnostic Testing I
Diagnostic Testing
Violation of Linearity
Violation of Full Rank
Violation of Regression Model
Test for the Violations
Test for the Violation of Linearity
Test for the Violation
Consequences of the Violation
Test for the Violation
Check Understanding of Diagnostic Testing
Solving Violations of Linearity
Solving Collinearity
Solving Endogeneity
Knowledge Check: Diagnostic Testing I
Diagnostic Testing II
Stochastic Regressors
Non-Normal Errors
Consequences of the Violations
Test for the Violations
Consequences of the Violation
Test for the Violation
Consequences of the Violation
Consequences of the Violation
Congratulations
Understanding Heteroscedasticity
Understanding Autocorrelation
Understanding Stochastic Regressors
Understanding Non-Normal Errors
Understanding Hypothesis Testing
Knowledge Check: Diagnostic Testing II
Hypotheses Testing in Econometrics at Coursera Admission Process
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