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Hypotheses Testing in Econometrics
  • Level Professional
  • Duration 27 hours
  • Course by Queen Mary University of London
  • Offered by

About

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. Before starting this course, it is expected that you have an understanding of some basic statistics, including mean, variance, skewness and kurtosis. It is also recommended that you have completed and understood the previous course in this Specialisation: The Classical Linear Regression model. 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.

Modules

Linearity of the OLS Estimator

    • 3 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Linearity

3 Videos

  • Welcome to Hypotheses Testing in Econometrics
  • Properties of the OLS Estimator
  • Presentation of Linearity

1 Readings

  • Understanding Linearity of the OLS Estimator

Unbiasedness of the OLS Estimator

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Check Your Understanding of Unbiasedness

1 Discussions

  • The Importance of Unbiasedness

1 Videos

  • Unbiasedness

1 Readings

  • Understanding Unbiasedness

Efficiency of the OLS Estimator

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Check Your Understanding of Efficiency

1 Discussions

  • The Importance of Efficiency

1 Videos

  • Efficiency

1 Readings

  • Understanding Efficiency

Consistency of the OLS Estimator

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 2 Assignment
Show info about module content

2 Assignment

  • Check Your Understanding of Consistency
  • Knowledge Check: Properties of the OLS Approach

1 Discussions

  • Exploring Consistency

1 Videos

  • Consistency

1 Readings

  • Understanding Consistency

Hypothesis Testing

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Building a Hypothesis

1 Discussions

  • The Importance of Hypothesis Testing

1 Videos

  • Hypothesis Testing

1 Readings

  • Using Hypothesis Testing

The t-Test

    • 1 Videos
    • 2 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Interpreting t-Tests

1 Labs

  • Example t-Test with R

1 Videos

  • The t-Test

2 Readings

  • Exploring the Test of Significance
  • Example of the t-Test

The F-Test

    • 1 Videos
    • 2 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Conditions for the f-Test

1 Labs

  • Example F-Test with R

1 Videos

  • The F-Test

2 Readings

  • Test Joint Hypothesis
  • An Example

Expanding on the t and F-Tests

    • 1 Videos
    • 1 Readings
    • 4 Assignment
Show info about module content

4 Assignment

  • Differences between t and F-Tests
  • Non-Nested Models
  • Check Your Understanding of Hypothesis Testing
  • Knowledge Check: Hypothesis Testing

1 Videos

  • Type I and Type II Errors

1 Readings

  • Types of Errors

Diagnostic Testing

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Check Understanding of Diagnostic Testing

1 Discussions

  • The Importance of Studying Violations of Assumptions

1 Videos

  • Diagnostic Testing

1 Readings

  • Test for the Violations

Violation of Linearity

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Solving Violations of Linearity

1 Discussions

  • How Do You Solve Linearity?

1 Labs

  • Example of a Violation of Linearity with R

1 Videos

  • Violation of Linearity

1 Readings

  • Test for the Violation of Linearity

Violation of Full Rank

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Solving Collinearity

1 Discussions

  • How Do You Solve Collinearity?

1 Labs

  • Example of Collinearity with R

1 Videos

  • Violation of Full Rank

1 Readings

  • Test for the Violation

Violation of Regression Model

    • 1 Videos
    • 2 Readings
    • 1 Discussion
    • 2 Assignment
Show info about module content

2 Assignment

  • Solving Endogeneity
  • Knowledge Check: Diagnostic Testing I

1 Discussions

  • How Do You Solve Endogeneity?

1 Videos

  • Violation of Regression Model

2 Readings

  • Consequences of the Violation
  • Test for the Violation

Heteroscedasticity

    • 2 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Understanding Heteroscedasticity

1 Discussions

  • Importance of Testing for Heteroscedasticity

1 Labs

  • Example of Heteroscedasticity with R

2 Readings

  • Consequences of the Violations
  • Test for the Violations

Autocorrelation

    • 2 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Understanding Autocorrelation

1 Discussions

  • How Do You Solve Autocorrelation?

1 Labs

  • Example of Autocorrelation with R

2 Readings

  • Consequences of the Violation
  • Test for the Violation

Stochastic Regressors

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Understanding Stochastic Regressors

1 Discussions

  • How Do You Solve Stochastic Regressors?

1 Videos

  • Stochastic Regressors

1 Readings

  • Consequences of the Violation

Non-Normal Errors

    • 1 Videos
    • 2 Readings
    • 1 Discussion
    • 1 PeerReview
    • 1 Lab
    • 3 Assignment
Show info about module content

3 Assignment

  • Understanding Non-Normal Errors
  • Understanding Hypothesis Testing
  • Knowledge Check: Diagnostic Testing II

1 Peer Review

  • Hypothesis and Diagnostic Testing

1 Discussions

  • How Do You Solve Non-Normal Errors?

1 Labs

  • Example of Normality with R

1 Videos

  • Non-Normal Errors

2 Readings

  • Consequences of the Violation
  • Congratulations

Auto Summary

This professional-level course, "Hypotheses Testing in Econometrics," focuses on the use of Classical Linear Regression Model (CLRM) parameters for hypothesis testing in uncertain contexts. Led by Coursera, it delves into statistical properties, null and alternative hypotheses, and diagnostic testing, utilizing R for practical applications. The course spans approximately 27 hours and offers Starter and Professional subscription options. Ideal for learners with a basic understanding of statistics and prior knowledge of CLRM, it aims to enhance decision-making skills through rigorous hypothesis testing techniques.

Dr Leone Leonida
Instructor

Dr Leone Leonida