Métodos de Previsão


The main objective of this course is to develop the skills needed to do empirical research in fields operating with time-series data sets.
The course intends to meet two goals: it provides tools for empirical work with time series data and is an introduction into the theoretical foundation of time series models. Much of the statistical methodology is concerned with models in which the observations are assumed to be independent. However, many data sets occur in the form of time series where observations are dependent. In this course, we will concentrate on time series analysis, with a balance between theory and applications.

Caracterização geral





Professor responsável


Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês


Statistics and linear algebra (recomended)


Método de ensino

The couse is based upon lectures and lab classes

Método de avaliação

•    (60%) Final exam (1st or 2nd round dates)
•    (40%) Project


1.    Time series basics: overview, autocorrelation and AR(1) model
2.    R tutorial
3.    Moving Average (MA) models and partial autocorrelation
4.    ARIMA Models: non-seasonal ARIMA models; diagnostics; forecasting
5.    Seasonal ARIMA models; identification
6.    Decomposition models
7.    Exponential smoothing
8.    The Periodogram
9.    Regression with ARIMA errors
10.    Two time series and cross-correlation
11.    Var models
12.    ARCH and GARCH models
13.    Longitudinal analysis
14.    Intervention analysis