Aviso: Se está a ler esta mensagem, provavelmente, o browser que utiliza não é compatível com os "standards" recomendados pela W3C. Sugerimos vivamente que actualize o seu browser para ter uma melhor experiência de utilização deste "website". Mais informações em webstandards.org.

Warning: If you are reading this message, probably, your browser is not compliant with the standards recommended by the W3C. We suggest that you upgrade your browser to enjoy a better user experience of this website. More informations on webstandards.org.

Sub Menu
ISCTE-IUL  >  Education  >  LG

Forecasting Methods (2 º Sem 2016/2017)

Code: L0109
Acronym: L0109
Level: 1st Cycle
Basic: No
Teaching Language(s): Portuguese, English
Friendly languages:
Be English-friendly or any other language-friendly means that UC is taught in a language but can either of the following conditions:
1. There are support materials in English / other language;
2. There are exercises, tests and exams in English / other language;
3. There is a possibility to present written or oral work in English / other language.
1 6.0 0.0 h/sem 36.0 h/sem 0.0 h/sem 0.0 h/sem 0.0 h/sem 0.0 h/sem 1.0 h/sem 37.0 h/sem 113.0 h/sem 0.0 h/sem 150.0 h/sem
Since year 2012/2013
Pre-requisites Basic statistics
Objectives The main purpose is to gain knowledge and experience in order to obtain good quality forecasts for cross-section and time series data (univariate and multivariate).
Program 1.Introduction
1.1 Forecasting needs and the importance of forecasting in the enterprise.
1.2 The different forecasting methods.
1.3 Choosing a forecasting method. Guidelines.
2. Causal models
2.1 The classical model of linear regression.
2.2 Extensions of the classical model. Violation of the basic assumptions - heteroscedasticity, autocorrelation and multicolinearity.
2.3 Other topics: Dummy variables, nonlinear models, information criteria: AIC and SBC, Wald, Likelihood ratio and Lagrange Multiplier tests
3. Time Series models
3.1 Decomposition methods.
3.2 Smoothing methods.
3.3 Auto-regressive and moving average models. The Box-Jenkins methodology.
Evaluation Method Continuous evaluation includes:
- a written individual test (35%);
- an individual report (35%);
- a group (3 or 4 elements) coursework (30%).

Continuous evaluation requires an attendance of at least 80% of classes. Approval means a final average grade equal or more than 10, provided that they did not had a grade lower than 7.5 in exam.


The evaluation can be made through a final examination. Students that obtain a grade between 7.5 and 9.5 can undergo an oral examination to pass.
Teaching Method During the learning-teaching term each student should acquire analytical, information gathering, written and oral communication skills, according to the established learning outcomes for this unit.
To contribute to the acquisition of these skills the following learning methodologies (LM) will be used:


1. Expositional
2. Participative
3. Active
4. Experimental laboratory
5. Self-study
Observations
Basic Bibliographic Brockwell, P.J. & R. A. Davis (2002), Introduction to Time Series and Forecasting, New York: Springer.
Hair, Jr., J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
Complementar Bibliographic Gujarati, D. - Basic Econometrics, McGraw-Hill, N. York 1995 (3ªEd.)
Greene, William (2002), Econometric analysis, Prentice-Hall, Fourth edition.
Johnston, J. e Dinardo, John (2000), Métodos econométricos, McGraw-Hill, 4ª edição.
Makridakis, S. e Wheelwright,S. - Forecasting Methods for Management J.Wiley, N.York 1989
Wooldridge, Jeffrey (2005), Introductory Econometrics : A Modern Approach.
Sherden, W., The Fortune Sellers: The Big Business of Buying and Selling Predictions, J. Wiley & Sons, N. York 1998.
Pindyck,R. e Rubinfeld,D. - Econometric Models & Economic Forecasts
McGraw-Hill, N.York 1991.