Sumários
Lecture 12
6 Dezembro 2022, 18:00 • Paulo Parente
Maximum Likelihood Estimation of the Linear Regression Model
- Properties of ML estimators;
- Estimators of the information matrix
- Hypothesis testing.
- Regressors
- The linear regression model
Exercise Sheet 8: Exercises 1 and 2
Lecture 11
29 Novembro 2022, 18:00 • Paulo Parente
Topic 8 - Instrumental Variables Estimation
- Two-Stage Least Squares Estimation (2SLS)
- Specification testing
Exercise Sheet 7: Exercises 1,2,3.
Topic 9 - Maximum Likelihood Estimation of the Linear Regression Model
- Likelihood function and the ML principle;
Lecture 10
22 Novembro 2022, 18:00 • Paulo Parente
Topic 7- Generalised Regression Model and Heteroskedasticity
- Conditional Heteroskedasticity
Exercise Sheet 6: Exercise 1,2,3
Topic 8 -Instrumental Variables Estimation
- Correlation between error terms and regressors
- Instrumental Variables
Lecture 9
15 Novembro 2022, 18:00 • Paulo Parente
Exercise Sheet 5: Exercises 1d,e, 2.
Generalised Regression Model and Heteroskedasticity
- The Generalised Regression Model
- Ordinary Least Squares Estimator
- The Generalised Least Squares (GLS) Estimator
- Feasible GLS (FGLS)
- Conditional Heteroskedasticity
Lecture 8
8 Novembro 2022, 18:00 • Paulo Parente
Asymptotic Theory
- Asymptotic normality
- Asymptotic theory for vectors and matrices
- Asymptotic properties of the least squares estimator
- Consistent estimation of the OLS variance covariance matrix
- Hypothesis Testing
Exercise Sheet 5: Exercises 1a,b,c