1. Review of your knowledge from the Statistics course.
2. Introduction to the subject.
- The simple linear model;
- Least Squares regression with one explanatory variable;
- Derivation of the regression coefficients;
- Interpretation of a regression equation;
- Goodness of fit: R2.
1. Open STATA ;
2. Download Data file;
3. Open files with exercises and start with exercise 1.4.
4.. Download available do files as an example.
- Assumptions for regression models with not-stochastic regressors.
- The random components and unbiasedness of the OLS regression coefficients .
- Precision of the regression coefficients.
- Testing hypotheses relating to the regression coefficients.
- The F test of goodness of fit.
- Derivation and interpretation of the multiple regression coefficients;
- Properties of the multiple regression coefficients;
- Godness of fit: R2;
- Open the Study guide for chapter #3;
- Solve the exercises A3.1, A3.2, A3.3;
- Open additional exercise and solve the following 3.7;3.8; 3.10 and 3.14.
- Linearity and non-linearity;
- Logarithmic transformations;
- Models with quadratic and interactive variables;
- Non-linear regression.
- Illustration of the use of a dummy variable;
- Extension to more than two categories and to multiple sets of dummy variables;
- Slope dummy variables;
- The Chow test
- Heteroscedasticity and its implications;
- Detection of heteroscedasticity;
- Remedies for heteroscedasticity;
- The problem of autocorrelation;
- Testing autocorrelation: Durbin-Watson and Breush-Godfrey tests;
- Remedies for autocorrelation.
- Testing the Normality assumption.