Sumários

Lecture 8

9 Novembro 2021, 18:00 • Paulo Parente

 Asymptotic Theory

  ∙  Consistent estimation of the OLS variance covariance matrix

  ∙  Hypothesis Testing

   Exercise Sheet 5: Exercises 1 and 2

  Generalised Regression Model and Heteroskedasticity

  ∙  The Generalised Regression Model
  ∙  Ordinary Least Squares Estimator
  ∙  The Generalised Least Squares (GLS) Estimator

Lecture 7

2 Novembro 2021, 18:00 • Paulo Parente

Exercise Sheet 4: Exercise 4 (c)

Asymptotic Theory

  ∙  Modes of convergence
  ∙  Consistency
  ∙  Asymptotic normality
  ∙  Asymptotic theory for vectors and matrices
  ∙  Asymptotic properties of the least squares estimator
  ∙  Consistent estimation of the OLS variance covariance matrix

Lecture 6

26 Outubro 2021, 18:00 • Paulo Parente

Exercise Sheet 3: Exercises 4(c),(d)

    Specification Analysis

  ∙  Omitted (Exclusion of Relevant) Variables
  ∙  Wrongly Included Variables
  ∙  Linear Regression Models with Logarithmic Transformations
  ∙  Functional Form - The RESET test
  ∙  Testing for Structural Change

Exercise Sheet 4: Exercises 1,2,3, 4(a),(b)

Lecture 5

19 Outubro 2021, 18:00 • Paulo Parente

Inference and Prediction in the Linear Regression Model

  ∙  Hypothesis testing in the Classical Regression Model
    -  Multiple restrictions
          *  The F-statistic
  ∙  Prediction
  ∙  Random Regressors under the normality assumption
Exercise Sheet 3: Exercises 1, 2, 3, 4(a),(b)

Lecture 4

12 Outubro 2021, 18:00 • Paulo Parente

Exercise Sheet 2:Exercise 5 (e) to (i) and Exercise 6

Inference and Prediction in the Linear Regression Model

  ∙  Measures of goodness of fit
  ∙  Multivariate normal
  ∙  Hypothesis testing in the Classical Regression Model
      -  Estimation of the variance of the OLS estimator
      -  Single restriction hypothesis
      -  Multiple restrictions
          *  The F-statistic