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General information |
Course unit name: Econometric Methods
Course unit code: 361238
Academic year: 2025-2026
Coordinator: Luis Miguel Guirola Abenza
Department: Department of Econometrics, Statistics and Applied Economics
Credits: 6
Single program: S
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Estimated learning time |
Total number of hours 150 |
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Face-to-face and/or online activities |
60 |
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- Lecture with practical component |
Face-to-face |
30 |
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- IT-based class |
Face-to-face |
30 |
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Supervised project |
40 |
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Independent learning |
50 |
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Learning objectives |
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Referring to knowledge The general objective of this course is to ensure students master the main econometric techniques used today by professionals, both in economics, business and other disciplines to complete applied research tasks.
Know and understand the tools and analytical techniques associated with the use of the multiple linear regression model.
Identify the properties of the different estimation methods of the multiple linear regression model and know the advantages and disadvantages of each.
Referring to abilities, skills Interpret rigorously and correctly the results of the estimation of a multiple linear regression model in its possible specifications.
Identify, for each particular model, which of the usual estimation hypotheses are more reasonable and which are less so.
Critically evaluate the conclusions drawn from a regression model, taking into account the properties of the variables analysed and the characteristics of the available data.
Apply the correct work guidelines in each of the steps required when using a multiple linear regression model: specification, estimation, validation and interpretation.
Referring to attitudes, values and norms Develop an interest in analysis and applied research based on the use of econometric and modeling techniques. |
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Teaching blocks |
1. Introduction
1.1. Concept and strategy of econometric research
1.2. Economic models and econometric models. Components and typology
1.3. Stages in econometric research
2. The multiple linear regression model: specification and estimation
2.1. Model specification
2.2. Basic hypotheses of the standard multiple linear regression model
2.3. Estimation for ordinary least squares (OLS)
2.4.
OLS estimation properties
2.5. Maximum-likelihood estimation
3. The multiple linear regression model: validation and prediction
3.1. Measures of the model’s goodness of fit
3.2. Hypothesis testing
3.3. Estimation with linear constraints
3.4. Variance analysis
3.5. Point and interval prediction
4. Specification errors and data issues
4.1. Functionally detected errors
4.2. Erroneous specification of explanatory variables
4.3. Permanence vs structural change
4.4. Multicollinearity
4.5. Detection of atypical and influential data
5. Failure to comply with the basic assumptions of the perturbation term
5.1. Matrices of scalar and non-scalar variances and covariances
5.2. Estimation by ordinary least squares (OLS) and properties
5.3. Estimation by generalized least squares (GLS) and properties
5.4. Estimation for maximum likelihood and properties
6. Heteroscedasticity
6.1. Definition and causes
6.2. Consequences of estimation by ordinary least squares (OLS)
6.3. Detection of heteroscedasticity
6.4. Estimation for generalized least squares (GLS) and weighted least squares (WLS)
6.5. Inference and prediction
7. Autocorrelation
7.1. Definition and causes
7.2. Consequences of estimation by ordinary least squares (OLS)
7.3. Autocorrelation detection
7.4. Estimation by generalized least squares (GLS)
7.5. Inference and prediction
8. Models of the discrete dependent variable
8.1. Linear probability model
8.2. Probit model
8.3. Logit model
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Official assessment of learning outcomes |
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Continuous assessment
Examination-based assessment The single assessment consists of two parts: |
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Reading and study resources |
Check availability in Cercabib
Book
GREENE, William H. Análisis econométrico. Madrid: Prentice Hall, 1999
WOOLDRIDGE, Jeffrey M. Introducción a la Econometría. Un enfoque moderno. 4a ed. revisada, Cengage Learning, 2016
STOCK, James H.et al. Introducción a la Econometría. 3a ed. Madrid: Pearson, 2012