Public University of Navarre



Academic year: 2022/2023 | Previous academic years:  2021/2022  |  2020/2021  |  2019/2020  |  2018/2019 
International Bachelor's degree in Management and Business Administration
Course code: 174403 Subject title: ECONOMETRICS
Credits: 6 Type of subject: Mandatory Year: 2 Period: 2º S
Department:
Lecturers
HUALDE BILBAO, JAVIER (Resp)   [Mentoring]

Partes de este texto:

 

Module/Subject matter

Quantitative Methods: Econometrics.

Up

General proficiencies

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.

Up

Specific proficiencies

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.

Up

Learning outcomes

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.

Up

Methodology

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 -  

Up

Relationship between formative activities and proficiencies

Activities Evaluated proficiencies Weight (%)
Preparation and presentation of reports and exercises CG04, CG05, CG06, CG07, CG16, CE02, CE03 y CE04 10%
Partial exams (theoretical and computer-based) CG04, CG07, CG16, CG17, CE02, CE03 y CE04 20%-20%
Final exam CG04, CG07, CG16, CG17, CE02, CE03 y CE04 50%

Up

Languages

English.

Up

Evaluation

 

Learning
outcome
Assessment
activity
Weight (%) It allows
test resit
Minimum
required grade
R18. Simple regression and explanatory variables models Theoretical tests 30% Yes None
R18. Simple regression and explanatory variables models Tests in computer classroom 30% No None
R18. Simple regression and explanatory variables models Final exam 40% Yes None

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

Up

Contents

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 give a brief introduction to two extensions of the previous model: regression with panel data and regression with a binary dependent variable.

Up

Agenda

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. Regression with panel data

Panel data

Panel data with two time periods: "Before and after" comparisons

Fixed effects regression

Regression with time fixed effects

 

Chapter 5. Regression with a binary dependent variable

Binary dependent variables and the linear probability model

Probit and Logit regression

Up

Bibliography

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)

Up

Location

Classroom and Computer room at the Aulario.

Up