Cursos / 1º Ciclo / Faculty of Economy and Business Management :: Economics
ECONOMETRIA - 2018/2019
2º curricular year
Semestralidade: 2nd semester
Leading Teacher: Prof. Doutor Francisco Vitorino Martins
Assistant Professor: Prof. Doutor Francisco Vitorino Martins
Class type and School hours
Orientação Tutorial : 1 Horas
Teórico-prática : 2 Horas
The main objectives of this undergraduate course in Economics are to prepare the students for the procedures of measurement and validation of econometric models, and to practice applied econometric models. With this course students learn to build and analyse econometric models.
Students must understand: 1) the structure of econometric models and their hypothesis; 2) databases and the use of metric and qualitative variables; 3) measurement of economic parameters (estimation methods); 4) tests of hypothesis and explicative variables selection; 5) forecasting and simulation; 6) the use of specialized software (Eviews).
Skills to be acquired
1) To read and to interpret econometric models published in the specialized literature.
2) To build econometric models for developing applied economic studies.
3) Initiation to research in economics.
4) To understand the value of economic information systems and their methodological interest (databases, samples).
5)To use professional software - EVIEWS - for economic and econometric analysis.
The methodology of teaching and learning is based on the study of theory, exercises (technical and applied) and in the case study method through specialized software.
Students must do, autonomously, practical exercises, analyse estimated econometric models proposed by the teacher, and build databases and econometric models through specialized software.
1. Uses of Econometrics
2. The object of Econometrics
3. The methodology of Econometrics
4. Dificulties of formalisation
1. The Classical Multiple Linear Regression Model
1.1.1 Population Regression Function (PRF) and Sample Regression Function (SRF)
1.1.2 Simple Linear Regression Model
1.1.3 Least Squares Method (LS). Coefficients estimation
1.1.4 Non linear models
1.1.5 Properties of the sample regression function
1.1.6 Coefficient of determination
1.1.7 Hypothesis of the Classical Multiple Linear Regression Model
1.1.8 Properties of the coefficients Least Squares estimators
1.1.9 Errors variance estimator. Properties
1.1.10 Dummy variables. Codification and coefficients variation
1.2 Statistical Inference
1.2.1 Probability distributions related to the coefficients and errors variance estimators
1.2.2 Confidence intervals for coefficients and errors variance
1.2.3 Individual test of coefficients. Individual significance test
1.2.4 Test of global significance
1.2.5 Test of variables´ addition (subset of coefficients)
1.2.6 Test of linear combination of coefficients
1.2.7 Test of structure permanence (Chow)
1.3.1 Forecasting of the dependente variable value. Hypothesis
1.3.2 Point and interval forecasting. Forecasting error probability distribution
1.3.3 Ex-ante and ex-post forecasting. Indicators.
2.1 The concept of autocorrelation
2.2 Estimation by Ordinary Least Squares (OLS) : consequences
2.3 Durbin-Watson statistic
2.4 Test of autocorrelation of Breusch-Godfrey
2.5 Parametrs estimation by OLS with correction of Newey-West and Non Linear Least Squares (NLLS)
3.1 The concept of heteroscedasticity
3.2 Tests of heteroscedasticity of White
3.3 Estimation of the parametrs by OLS with White correction and Weighted Least Squares (WLS)
2 tests and a global exam
Software Eviews and other common resources of the classroom, namely computers.
Least Squares Method
Tests and inference
Forecasting and simulation
|Author||M. Mendes de Oliveira, F. Vitorino Martins et al.|
|Title||Elements of Econometrics|