Econometrics and Forecasting Methods

Code:

LGE307

Acronym:

MEP

Keywords
Classification	Keyword
OFICIAL	Economics

Instance: 2006/2007 - 2S

Active?	Yes
Responsible unit:	Grupo de Economia
Course/CS Responsible:	Business Administration

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
GES	268	Official Curricular Structure since 2004	3	3		-

Objectives

- To know the basic concepts of econometric models and time-series models;
- to develop econometric models on Management subjects (Finance, Marketing, etc.);
- to study the estimation, inference and forecasting techniques;
- to analyse critically models hypothesis and results;
- to use professional software- Econometric Views
- estimation and analysis of 10 cases using real data

Program

Introduction
1- Utility of econometrics 2- Object and methodology
3- Forecasting and time series models

1-Classical Multiple Linear Regression Model
11 Estimation
111 Population and Sample Regression Functions
112 Simple Linear Regression Model
113 Least Squares Method
114 Non-linear models
115 Properties of the sampling regression function
116 Coefficient of determination
117 Hypothesis of the Classical Model
118 Least squares estimation : properties
119 Estimation of the disturbances variance
1110 Maximum likelihood : estimation and properties
1111Dummy variables. Codification and variation of the regression coefficients
12 Statistical inference
121 Probability distributions about regression coefficients and disturbances variance
122 Confidence intervals for regression coefficients
123 Individual tests for regression coefficients. Individual test of significance
124 Global significance test. ANOVA- Analysis of variance
125 Test of a subset of regression coefficients
126 Linear combination of regression coefficients
127 Chow structural test
13 Forecasting
131 Forecasting of the level of the dependent variable. Hypothesis
132 Point and interval forecasting. Forecasting error probability distribution
133 Forecasting indicators. Ex-ante and ex-post forecasting
134 Seasonality regression models

2- Autocorrelation
21 Introduction
22 Ordinary Least Squares estimation. Proprieties and inference validity
23 Processes AR(1) and MA(1). Autocorrelation Functions
24 Test of autocorrelation : Durbin-Watson and Breusch-Godfrey
25 Autocorrelation estimation : Cochrane- Orcutt iterative method and maximum likelihood

3- ARIMA Models (Box-Jenkins)
3.1 Stationarity. Transformation of series
3.2 Box-Jenkins methodology - identification, estimation, diagnostic-checking and forecasting

Main Bibliography

*M. Mendes de Oliveira, Álvaro Aguiar, Armindo Carvalho, F. Vitorino Martins, Víctor Mendes and Pedro Portugal, Econometria, McGraw-Hill, 1997
**Gujarati, Basic Econometrics, Mc Graw Hill, 2003.
**Hanke, Wichern, Reitsch, Business Forecasting, Prentice Hall, 2001
*F. Vitorino Martins - "Ten cases on econometric modelling - data, questions and resolutions on Eviews", 2005
*=obligatory **=recommended

Complementary Bibliography

Kmenta, Jan, Elements of Econometrics, Maxwell MacMillan, 1990
Pindyck, R.S., and Rubinfeld,D.L., Econometric Models and Economic Forecasts, McGraw-Hill, 1998.

Teaching methods and learning activities

Theoretical explanations, exercises (technical and applied) and cases in Eviews

Software

Econometric Views

Evaluation Type

Distributed evaluation with final exam

Eligibility for exams

Attending at least 80% of classes (for continuous evaluation)

Calculation formula of final grade

50%*score test 1+50%*score test 2
or 100% exam score

Classification improvement

Yes