Master in Business and Finance (Plan: 362 - Estudio: 69)
EPE
Coordinating teacher: MEILAN VILA, ANDREA
Department assigned to the subject: Statistics Department
Type: Compulsory
ECTS Credits: 5.0 ECTS
Course: 1º
Semester: 1º
Requirements (Subjects that are assumed to be known)
Statistics for Economics and Business
Objectives
The goal of this course is to familiarize students with regression models and achieve the following learning objectives:
- Apply the theory of least squares to solve linear regression problems.
- Use diagnostic techniques to determine whether a regression model fits a given dataset properly.
- Implement regression models using statistical software R.
- Conduct an analysis of the temporal properties of a statistical series.
1. Introduction
1.1. Regression models.
1.2. Simple linear regression.
1.2.1. Formulation of the model.
1.2.2. Model assumptions.
1.2.3. Parameter estimation.
1.2.4. The F test.
1.2.5. Prediction.
1.3. Statistical Software R.
2. Multiple linear regression: estimation, confidence regions and hypothesis testing.
2.1. The general linear model.
2.1.1. Formulation of the model.
2.1.2. Analysis of variance (ANOVA) model.
2.1.3. Model assumptions.
2.2. Parameter estimation.
2.3. Inference about the parameters.
2.4. Variability decomposition. The F test.
2.5. Prediction.
3. Validation of a regression model.
3.1. The determination coefficient.
3.2. Model diagnosis.
3.3. Regression transformations.
4. Diagnosis of outliers or influential observations. Construction of regression models.
4.1. Diagnostic techniques.
4.1.1. Leverages.
4.1.2. Detection of outliers and influential observations.
4.1.3. Dealing with outliers or influential observations.
4.2. Construction of regression models.
4.2.1. Polynomial regression.
4.2.2. Interactions.
4.2.3. Collinearity.
4.2.4. Variable selection methods.
5. Generalized least squares.
5.1. Introduction.
5.2. Generalized least squares.
5.3. Weighted least squares.
5.4. Iteratively reweighted least squares.
5.5. Feasible generalized least squares.
6. Time series models.
6.1. Autoregressive (AR) and moving average (MA) models.
6.2. ARMA and ARIMA models.
Learning activities and methodology
The course is organized in theoretical classes, whose materials are slides, exercise classes and computer classes, where R will be used in order to illustrate and consolidate the contents.
Assessment System
% end-of-term-examination 50
% of continuous assessment (assigments, laboratory, practicals...) 50
