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CA  >  Actuarial Science  >  Currículo  >  Time Series

Master in Actuarial Science

Plano Curricular Actuarial Science

Time Series (MP-CA)

UC Competência

Time Series(Matemática)

UC Execução

Time Series (2020/2021 - Semestre 2)
Time Series (2019/2020 - Semestre 2)
Time Series (2018/2019 - Semestre 2)
Métodos de Previsão (2017/2018 - Semestre 2)
Métodos de Previsão (2016/2017 - Semestre 2)
Métodos de Previsão (2015/2016 - Semestre 2)
Métodos de Previsão (2014/2015 - Semestre 2)
Métodos de Previsão (2013/2014 - Semestre 2)
Métodos de Previsão (2012/2013 - Semestre 2)
Métodos de Previsão (2011/2012 - Semestre 2)


Grupo: Actuarial Science > 2º Ciclo > Unidades Curriculares Obrigatórias

Período: 1 Ano, 2 Semestre


6.0 (para cálculo da média)


On completion of this course, the student should be able to:
- Recognize and understand the main econometric models used in the analysis of time series.
- Understand the theoretical reasoning which led to the development of the most important univariate and multivariate models.
- Be familiar with the use of econometric software to carry out time series analysis.
- Develop critical thinking about empirical work with time series data.
- Be able to develop a forecasting study of different sets of variables and formulate statistical hypotheses of interest. Understand the limitations of the econometric methodology applied in the study


- Introduction to time series analysis. Fundamental concepts
- Models for stationary time series. Autoregressive Moving Average (ARMA) models
- Box-Jenkins methodology: model identification, estimation and diagnostic checking
- Models for nonstationary time series. Autoregressive Integrated Moving Average (ARIMA) models and unit root testing
- Forecasting using ARIMA models
- Seasonality and Seasonal ARIMA (SARIMA) models
- Conditional Heteroskedasticity time series models. ARCH/GARCH models
- Forecasting with exponential smoothing methods
- Multivariate Time Series Models

Metodologia de avaliação

Lectures will be theoretical and practical, starting on main empirical patterns found in time series as a basis to present statistical methods and models used to represent it. Core mathematical models for time series will be presented in a constructive way, but practical relevance of different models in terms of time series behavioural patterns and on the nature of implied forecast functions will also be strengthened. Using available software, models and modelling strategies will be applied on real time series data with emphasis in critical analysis as a function of purposes.
Students will be assessed based on a final exam (60%) and a practical computational test (40%) using R



Applied Econometric Time Series

Enders, W.



Time Series Analysis

Hamilton, J.


Princeton University Press

Analysis of Financial Time Series

Tsay, R. S.



Análise de Séries Temporais

Morettin P. A., e C. M. C. Toloi


Editora Edgard Blücher

Introductory Econometrics: A Modern Approach

Wooldridge, J.M.


Cengage Learning


Não existem referências bibliográficas secundárias.