Nova School of Business and Economics

Econometria

Código

1306-124

Unidade Orgânica

null

Departamento

null

Créditos

7,5

Professor responsável

João Valle e Azevedo

Língua de ensino

English

Objectivos

null

Pré-requisitos

Linear Algebra
Statistics

Conteúdo

Introduction (Chapter 1 of the textbook)
1.1 What is Econometrics?
1.2 Phases of the Econometric Work
1.3 Economic Data
2. Simple Linear Regression Model (Chapter 2)
2.1 Basic notions and notation
2.2 Definition of the Simple Regression Model
2.3 Ordinary Least Squares (OLS)
2.4 Properties of the OLS Estimators
2.5 Estimating the variance of the error term
3. Multiple Linear Regression (Chapter 3)
3.1 The specification of the multiple linear regression model
3.2 The OLS estimator
1
3.3 Properties of the OLS Estimators
3.4 Omitted variable bias
3.5 Multicolinearity
4. Statistical Inference in the Multiple Regression (Chapter 4)
4.1 Hypothesis testing for individual coefficients
4.2 Confidence Intervals
4.3 Hypothesis testing for linear combinations of coefficients
4.4 Simultaneous hypothesis testing for coefficients
5. Asymptotic properties of the OLS estimator (Chapter 5)
5.1 Consistency
5.2 Large Sample Inference
5.3 Lagrange Multiplier Test
5.4 Residual Analysis
6. Further issues (Chapters 6 and 7)
6.1 Specification analysis
6.2 Prediction and residuals analysis
6.3 Explanatory binary variables
7. Heteroskedasticity (Chapter 8)
7.1 Nature and consequences of heteroskedasticity
7.2 Robust inference
7.3 The White test
7.4 Weighted Least Squares
8. Time series econometric models (Chapters 10 and 11)
8.1 Nature of time series
8.2 Properties of OLS
8.3 Time trends and seasonality
8.4 Stationarity and Non-stationarity: consequences
2
9. Serial correlation and Heteroskedasticity with Time Series (Chapter 12)
9.1 Properties of OLS
9.2 Detecting and correcting serial correlation
9.3 Robust inference

Bibliografia

Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach, 3rd (or
4th) Edition

Método de ensino

null

Método de avaliação

Final grade will be based on a midterm (weighted 25%), on a group assignment (25%)
and on the final exam (50%). In order to pass, you must obtain a grade higher or
equal to 8.0 (NOT 7.5 or 7.9) in the final exam. For those trying to improve their
grades, only the final exam counts.

Cursos