Econometrics

Group	Teacher	Department	Language
Year 3	Miquel Torrens Dinarès	Economía, Finanzas y Contabilidad	ENG

COURSE CONTRIBUTION TO PROGRAM

Solid understanding of basic econometrics and statistical modelling is a necessary skill to undertake any relevant analysis of the economic activity at any level. We will learn to develop models empirically to effectively analyse data in the real world, a very common practice in almost any economics profession.

Course Learning Objectives

The objective of the course is to understand how to use linear regression analysis properly, as a major tool ubiquitous in economic analysis. The course has three specific goals. First, introduce the basic econometric methodology in the context of the classical linear regression, particularly OLS estimation and inference. Secondly, help future practitioners become more sophisticated consumers of empirical work done by others, evaluate whether analyses are done in a proper manner, and develop their own scientific critical thinking. And third, to start to develop the student's capacity in statistical programming, which is a skill in increasing demand in social science professions.

CONTENT

1. Basics in Probability and Random variables Review of concepts already seen that are necessary to develop later topics
2. Hypothesis testing Mostly review of inference for single parameter models, such as the mean for continuous and binary variables, as well as count data.
3. Simple linear model Construction and derivation of the linear regression model with one variable.
4. Multiple linear model Extension of linear regression to any larger number of variables.
5. Inference in the linear model We evaluate the quality and validity of the fitted model, and study the uncertainty around the point estimates of our regression.
6. Violations of regression assumptions Study of why the assumptions of the linear model are relevant, what occurs in the event they do not hold, and how that can be addressed.
7. Qualitative data and model transformations Introduction of binary, categorical and non-linear transformations to the linear model.
8. Instrumental variables Introduction to "causal" inference: proper estimation of a linear model with relevant missing variables.
9. Logistic regression Introduction to generalised linear models: binary outcomes.
10. Further topics in econometrics - Differences in differences - Introduction to Bayesian modelling

1. Basics in Probability and Random variables

Review of concepts already seen that are necessary to develop later topics

2. Hypothesis testing

Mostly review of inference for single parameter models, such as the mean for continuous and binary variables, as well as count data.

3. Simple linear model

Construction and derivation of the linear regression model with one variable.

4. Multiple linear model

Extension of linear regression to any larger number of variables.

5. Inference in the linear model

We evaluate the quality and validity of the fitted model, and study the uncertainty around the point estimates of our regression.

6. Violations of regression assumptions

Study of why the assumptions of the linear model are relevant, what occurs in the event they do not hold, and how that can be addressed.

7. Qualitative data and model transformations

Introduction of binary, categorical and non-linear transformations to the linear model.

8. Instrumental variables

Introduction to "causal" inference: proper estimation of a linear model with relevant missing variables.

9. Logistic regression

Introduction to generalised linear models: binary outcomes.

10. Further topics in econometrics

- Differences in differences
- Introduction to Bayesian modelling

Methodology

Combination of
- Lecture notes (slides plus blackboard)
- Applied theoretical and practical exercises (short problem sets)
- Statistical programming (introduction to R)
- Short tests in selected topics to consolidate key knowledge
- Applied creative project

Assessment criteria

- 50% Final exam
- 25% Five short tests (5% each)
- 15% Small group project (10% report + 5% public defence)
- 10% Attitude and active involvement

There are two conditions that must be met to pass the course
- Score at least 50% of the global grade
- Score at least 40% in the final exam
To re-take the final exam, it is necessary to have scored at least 30% in the "first" final exam.
There will be six short tests distributed across the course, each counting for 5% of the grade -- the worst grade will be discarded for the final grade.
The group project will be conducted in couples, only one exception will be made if the number of students in the course is odd.
The attitude grade will be given according to the general involvement of the student in the course (preparation, problem sets, questions and quality of intervention).

Timetable and sections

Group	Teacher	Department
Year 3	Miquel Torrens Dinarès	Economía, Finanzas y Contabilidad

Timetable Year 3

From 2022/9/5 to 2022/10/5:
Monday and Wednesday from 9:00 to 11:30. (Except: 2022/9/26, 2022/9/28 and 2022/10/3)

From 2022/10/3 to 2022/11/22:
Each Monday from 9:00 to 11:30.
Each Tuesday from 18:30 to 20:00. (Except: 2022/10/4, 2022/10/11, 2022/10/18, 2022/10/25 and 2022/11/1)

From 2022/10/19 to 2022/11/23:
Each Wednesday from 9:00 to 11:30.
Each Wednesday from 18:30 to 20:00. (Except: 2022/10/19 and 2022/10/26)

From 2022/11/30 to 2022/12/5:
Each Wednesday from 14:30 to 16:00.
Each Monday from 9:15 to 12:30.
Each Friday from 16:00 to 17:30.

Thursday 2023/2/9 from 9:15 to 12:30.

Type:	OPT	Curs:	3	Period:	S semester
ECTS Credits:	7 ECTS

Econometrics (2225.YR.013014.1)

General information

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Prerequisites

Previous Knowledge