Introduction To Econometrics Ii

1. Apply statistical methods to measure economic relationships.
2. Understand fundamental techniques involving linear regression estimation.
3. Analyze the strengths and weaknesses of the basic regression model.
4. Identify and address issues like multicollinearity and heteroscedasticity.
5. Test hypotheses of model parameters and joint hypotheses.
6. Discuss the consequences of specifying equations incorrectly.

13 weeks, about 26 hours in total. Yours will differ.

1. Week 1Module 1: Sampling Theory, Variance, and Correlation

   • Unit 1: Random variables and sampling theory · 2 hours

     Define random variables and differentiate between discrete and continuous variables.. Understand the concept of a population and how samples are drawn for analysis.. Solve problems related to expected values of discrete random variables..

2. Week 2Module 1: Sampling Theory, Variance, and Correlation

   • Unit 2: Covariance and Variance · 2 hours

     Calculate sample covariance between two variables.. Apply covariance rules to solve problems.. Differentiate between population and sample covariance.. Calculate sample variance and apply variance rules..

3. Week 3Module 1: Sampling Theory, Variance, and Correlation

   • Unit 3: Correlation · 2 hours

     Calculate and interpret the correlation coefficient.. Analyze scattered diagrams to understand linear association.. Solve problems related to correlation coefficients..

4. Week 4Module 2: Regression Models, Hypotheses Testing, and Dummy Variables

   • Unit 4: Simple Regression Analyses · 2 hours

     Differentiate between simple and multiple regression models.. Understand the components of a simple regression model.. Identify reasons for including a disturbance term in the model.. Apply least squares regression to estimate coefficients..

5. Week 5Module 2: Regression Models, Hypotheses Testing, and Dummy Variables

   • Unit 5: Properties of the Regression Coefficients and Hypothesis Testing · 2 hours

     Understand the random components of regression coefficients.. Outline the assumptions concerning the disturbance term (Gauss-Markov conditions).. Test hypotheses relating to regression coefficients.. Calculate confidence intervals..

6. Week 6Module 2: Regression Models, Hypotheses Testing, and Dummy Variables

   • Unit 6: Multiple Regression Analysis and Multicollinearity · 2 hours

     Interpret multiple regression coefficients.. Understand the properties of multiple regression coefficients.. Perform t-tests and construct confidence intervals.. Understand the concept of multicollinearity..

7. Week 7Module 2: Regression Models, Hypotheses Testing, and Dummy Variables

   • Unit 7: Transformations of Variables · 2 hours

     Apply linear and non-linear transformations to variables.. Understand how transformations can achieve linearity in models.. Solve problems involving transformations of variables..

8. Week 8Module 2: Regression Models, Hypotheses Testing, and Dummy Variables

   • Unit 8: Dummy Variables · 2 hours

     Apply dummy variables to incorporate qualitative factors into regression models.. Avoid the dummy variable trap.. Change the reference category and interpret the results.. Use slope dummy variables to allow for varying slopes..

9. Week 9Module 2: Regression Models, Hypotheses Testing, and Dummy Variables

   • Unit 9: Specification of regression variables: A preliminary skirmish · 2 hours

     Understand the importance of model specification.. Identify the consequences of misspecifying equations.. Discuss tests used to identify correct model specification.. Apply statistical criteria for choosing between models..

10. Week 10Module 3: Heteroscedasticity/Heteroskedasticity

    • Unit 10: Heteroscedasticity and Its Implications · 2 hours

      Understand heteroscedasticity and its implications.. Identify likely sources of heteroscedasticity.. Apply tests to detect heteroscedasticity (Spearman rank correlation, Goldfeld-Quandt, Glejser)..

11. Week 11Module 3: Heteroscedasticity/Heteroskedasticity

    • Unit 11: Solution to Heteroscedasticity Problem · 2 hours

      Apply solutions to heteroscedasticity problems.. Understand the consequences of heteroscedasticity.. Solve problems related to heteroscedasticity..

12. Week 12Module 3: Heteroscedasticity/Heteroskedasticity

    • Unit 12: Other Tests · 2 hours

      Apply other tests for heteroscedasticity.. Understand the consequences of heteroscedasticity.. Solve problems related to heteroscedasticity..

13. Week 13Module 4: Autocorrelation, Error, and Econometric Modelling

    • Unit 13: Stochastic Regression and measurement errors · 2 hours

      Understand stochastic regressors and measurement errors.. Analyze the consequences of measurement errors.. Solve problems related to stochastic regressors and measurement errors..