Course code: 174403 |
Subject title: ECONOMETRICS |
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Credits: 6 |
Type of subject: Mandatory |
Year: 2 |
Period: 2º S |
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Department: Estadística, Informática y Matemáticas |
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Lecturers: |
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HUALDE BILBAO, JAVIER (Resp) [Mentoring ] | DEL VILLAR OLANO, ALBA MARIA [Mentoring ] |

First, we will introduce the idea of econometric model. This model must take into account the special features of economic data. We will focus on the ideas of causality and ceteris paribus analysis, arising from the correct interpretation of these models. From a formal viewpoint, the initial step towards an appropriate conceptual framework will be to introduce the simple regression model. Here, we will study the basic assumptions, interpretation of parameters of interest, ordinary least squares estimation and statistical properties of the estimators.

The second building block of the course is the study of the general regression model. We will analyze its basic properties, focusing on the motivation behind multivariate regression, stressing its usefulness over the bivariate framework which characterizes the simple regression model. Again, we will study in this more general framework the basic assumptions, interpretation of parameters of interest, ordinary least squares estimation and statistical properties of the estimators.

In the last part of the course, we will study different aspects related to the general regression model. First, we will analyze the consequences of the violation of basic assumptions (functional form and specification, heteroskedasticity, autocorrelation). Next, we will introduce the issue of endogeneity, its relation with the instrumental variables estimator (emphasizing the search for appropriate instruments in economics) and a brief discussion of this problem in the context of systems of equations. The course will end up with a brief introduction on qualitative information variables, focusing on dummy variables and on the linear probability model.

CG04. Oral and written communication in a foreign language.

CG05. Developing software knowledge applied to the corresponding subject.

CG06. Ability to analyze and extract information from different sources.

CG07. Capacity to solve problems.

CG09. Capacity to work in teams.

CG16. Capacity to work under pressure.

CG17. Capacity to self-learning.

CE02. Identify relevant economic information sources.

CE03. Derive from micro and macroeconomic data relevant information impossible to assess by non-specialists.

CE04. Use of professional criteria in the economic analysis, mainly those criteria based on technical tools.

R20. Simple regression and explanatory variables models, econometric models.

At the end of the course, the student must be able to translate into mathematical language specific issues from macroeconomics and microeconomics by means of econometric models. Additionally, the student must be able to use the econometric software Gretl and extract conclusions from the estimated models.

Lectures

Presentation of the main theoretical aspects of the course. Student participation: questions made by the lecturer, brief presentations of previous lectures.

Classes

Classes in computer room. Small groups. Presentation and revision of exercises made individually and in small groups. Use of econometric packages (GRETL).

Individual and team work

Preparation of exercises and presentations.

Periodic tutorials with lecturer

Individual and group meetings.

Personal study and exam

Activity | Hours | |

On-site | 60 | |

Lectures | 30 | |

Classes | 30 | |

Self-preparation | 90 | |

Self-study | 38 | |

Individual preparation of exercises and presentations | 26 | |

Team preparation of exercises and presentations | - | |

Exam preparation | 20 | |

Tutorials | 06 | |

Others | - |

Learning outcome | Evaluation Method | Weight (%) | Recoverable |

R18. Simple regression and explanatory variables models | Theoretical Tests | 25% | Recoverable |

R18. Simple regression and explanatory variables models | Participation and exposition in lectures | 10% | Non-recoverable |

R18. Simple regression and explanatory variables models | Tests in computer classroom | 25% | Non-recoverable |

R18. Simple regression and explanatory variables models | Final exam | 40% | Recoverable |

Those students who do not attend the final exam will get a grade of "No Presentado"

Chapter 1. Introduction: the nature of econometrics and economic data

What is econometrics?

Methodology in econometric analysis

The structure of economic data

Causality and the notion of ceteris paribus in econometric analysis

Chapter 2. The simple regression model

Definition of the simple regression model

Ordinary least squares estimator

Algebraic properties of the ordinary least squares estimator

Units of measurement and functional form

Statistical properties of the ordinary least squares estimator

Chapter 3. The general regression model

Motivation

Algebraic properties of the ordinary least squares estimator

Statistical properties of the ordinary least squares estimator

Gauss-Markov theorem

Hypotheses testing

Asymptotic properties

Prediction

Model selection

Chapter 4. Violation of assumptions

Specification errors

Multicollinearity

Heteroskedasticity

Autocorrelation

Chapter 5. Endogeneity and instrumental variables

Motivation

Instrumental variables estimator

**Access the bibliography that your professor has requested from the Library.**

The main references for the course are:

Wooldridge, J.M. "Introductory econometrics: a modern approach". South-Western College Pub; 4 edition (2008).

Stock, J.H. and Watson, M.W. "Introduction to econometrics". Prentice Hall (2010)

Alternative useful textbooks are:

Goldberger, A.S. "Introductory econometrics ". Harvard University Press (1998).

Gujarati, D.N. "Basic econometrics". Mc. Graw Hill (2004)