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: |
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Lecturers: |
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HUALDE BILBAO, JAVIER (Resp) [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 give a brief introduction to two extensions of the previous model: regression with panel data and regression with a binary dependent variable.

RA01: Identify relevant economic information sources for the company and their content.

RA02: Understand economic institutions as the result and application of theoretical or formal representations of how the economy works.

RA03: Derive relevant information for the company from data that cannot be recognized by non-professionals.

RA20: Plan, organize, and control global management projects or those of the different functional areas of the company.

RA25: Use personal, social, and methodological knowledge, skills, and abilities to issue advisory reports on specific situations of companies and markets.

RA29: Develop the skills necessary to understand and apply quantitative techniques to the interpretation and resolution of socioeconomic problems.

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 |
Assessment activity |
Weight (%) |
It allows test resit |
Minimum required grade |
---|---|---|---|---|

Theoretical tests and class participation | 25% | Yes* | None | |

Tests in computer classroom and class participation | 25% | Yes* | None | |

Final exam | 50% | Yes* | 3 out of 10 |

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

*recoverable through a single exam that will weigh 100% of the grade.

**Chapter 1. Introduction**

Economic questions we examine

Causal effects and idealized experiments

Data: Sources and types

**Chapter 2. Linear regression with one regressor**

The linear regression model

Estimating the coefficients of the linear regression model

Measures of fit and prediction accuracy

The least squares assumptions for causal inference

The sampling distribution of the OLS estimators

Testing hypotheses about one of the regression coefficients

Confidence intervals for a regression coefficient

Regression when X is a binary variable

Heteroskedasticity and homoskedasticity

The theoretical foundations of ordinary least squares

Using the t-statistic in regression when the sample size is small

**Chapter 3. Linear regression with multiple regressors**

Omitted variable bias

The multiple regression model

The OLS estimator in multiple regression

Measures of fit in multiple regression

The least squares assumptions for causal inference in multiple regression

The distribution of the OLS estimators in multiple regression

Multicollinearity

Control variables and conditional mean independence

Hypothesis tests and confidence intervals for a single coefficient

Tests of joint hypotheses

Testing single restrictions involving multiple coefficients

Confidence sets for multiple coefficients

Model specification for multiple regression

Analysis of the test score data set

A general strategy for modeling nonlinear regression functions

Nonlinear functions of a single independent variable

Interactions between independent variables

Nonlinear effects on test scores of the student-teacher ratio

Internal and external validity

**Chapter 4. Regression with panel data**

Panel data

Panel data with two time periods

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

Application to the Boston HMDA data

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

The main references for the course are:

Stock, J.H. and Watson, M.W. "Introduction to econometrics". Pearson Education Limited; 4 edition (2020).

Wooldridge, J.M. "Introductory econometrics: a modern approach". Cengage Learning Inc.; 7 edition (2020).