Introduction to Econometrics

Course outline

1. Econometric analysis: introduction

      The goals of econometric analysis

      Theory versus empirics

2. Linear regression with one explanatory variable

   Model and hypotheses 

   Parameter estimation

   Statistical inference

3. Some useful complementary results in linear algebra

    

4. Multiple linear regression model

   Model and hypotheses

   The Ordinary Least Square (OLS) estimator

   Testing simple and multiple hypotheses

   Prediction

5. Selected topics

The course aims at introducing students with the foundations of econometric analysis for both theory and practice. Econometrics builds a bridge between the formal models economist think with, and the data at disposal to estimate and validate them - a crucial step in modern economic analysis in a rich data environment. The focus of the course is on the workhorse model for econometrics, i.e. the linear multiple regression model, where the dependent variable is explained linearly in terms of  a set of covariates. In this model the course addresses estimation of the coefficients, hypothesis testing and prediction. We use the free statistical software R for practical implementation on real datasets. At the end of the course students are expected to be familiar with the multiple regression model both in terms of basic formal properties and its practical application on real data. 

• Quality education
• Decent work and economic growth

In presence

The course provides detailed lecture notes from the instructors that are presented in class. To familiarize students with concepts and methods, series of exercises are distributed and corrected regularly along the course. Some classes are dedicated to practical implementation of regression with the R software. 

Written exam.