Objectives

The objective of the class is to provide students with:

• an understanding the basic concepts of statistics and estimation
• an understanding of how to design an estimation strategy
• an understanding of how to efficiently implement it and then reach an interpretation of the results.

This class should be a "hands-on" class on dealing with data. It will be composed of two parts, in the first part of each session, we will go over econometric models; in the second part, we will use software such as STATA, python, etc to analyze related examples.

Contents

1. Introduction

Introduction to Econometrics

Tools used for economic analysis

2. The Linear Regression Model

The Linear Regression Model

Least Squares Regression

Hypothesis Tests and Model Selection

3. Estimation Methodology

4. Cross Sections, Time Series and Panel Data

* Important: I might adjust the pace of the class and this syllabus according to our progress throughout the semester. Please pay close attention to the Moodle system.

References

Required Textbook:

W. Greene (2018), "Econometric Analysis" (8th edition, Prentice Hall).

Other (read if interested):

J. Hamilton (1994), "Time Series Analysis" (Princeton University Press).

W. McKinney (2013), "Python for Data Analysis" (O'Reilly Media Inc)

Evaluation

First attempt

Exam:

Written 1h30 hours

Documentation:

Allowed

Calculator:

Allowed

Evaluation:

Grading will be based on

Homework Assignments (30%) + Final Exam (70%).

Grading is done on a relative (not absolute) basis.

Retake

Exam:

Written 1h30 hours

Documentation:

Allowed

Calculator:

Allowed

Evaluation:

The retake will include several questions related to the class materials.

Other elements (homework, projects, etc.) remain acquired