Home » Departments and Centres » Academic catalogue » Course detail

Open sidebar menu

Statistics and Econometrics 1 (ECO-CO-STATS1)

ECO-CO-STATS1

Description

The main goal of this core course is to introduce the basic tools that an econometrician needs: the most popular estimation methods; inference and hypothesis testing; asymptotics; simple and multiple regression; instrumental variables. In addition to the lectures there will be six exercise classes. Examples and applications will be used to illustrate the theoretical content of the course.

Learning outcomes:     
By the end of this course, students will be able to:
•    Understand and apply fundamental concepts in statistical estimation, including maximum likelihood and method of moments.
•    Evaluate the finite sample and asymptotic properties of estimators.
•    Identify and choose between different estimators based on their theoretical properties.
•    Demonstrate knowledge of key asymptotic theorems
•    Estimate and interpret linear regression models using Ordinary Least Squares (OLS), Method of Moments (MM), and Maximum Likelihood Estimation (MLE).
•    Understand and apply the Gauss-Markov Theorem in both simple and multiple regression settings.
•    Identify and address potential issues in regression such as omitted variable bias and inclusion of irrelevant regressors.
•    Conduct hypothesis testing and inference using both finite sample and asymptotic methods.
•    Understand the theory and application of Instrumental Variable (IV) estimation.

Assessment    
•    Final Exam: Open-book and open-notes exam
•    Problem Sets: 3 problem sets distributed during the course.

Module structure

WEEK 1
Simple Regression 
Topics: 
•    The Conditional Expectation Function
•    The Population Regression Function
•    The Sample Regression Function
•    OLS, Method of Moments and Maximum Likelihood estimation of a regression 
•    Algebraic and geometric properties of the OLS- MM estimators
•    Goodness of fit and the R-Squared
•    Statistical Properties of the OLS-MM estimator
•    The Gauss-Markov Theorem
Angrist and Pischke chapter 1, 2 and 3. Wooldridge part 1. Lecture notes.

WEEK 2
Simple Regression - Continued; Multiple Regression
Topics: 
•    Simple Regression: Causality and Regression
•    The Conditional Independence Assumption 
•    Interpretation of the partial multiple regression coefficient 
•    Matching and regression
Angrist and Pischke chapter 1, 2 and 3. Wooldridge part 1. Lecture notes.

WEEK 3
Simple Regression - Continued; Multiple Regression
Topics: 
•    Multiple Regression in matrix notation 
•    Omitted variable bias and inclusion of irrelevant regressors
•    The Gauss-Markov Theorem and Multiple Regression 
•    “Partialling out” and the interpretation of coefficients 
•    Good and bad habits concerning control variables
Angrist and Pischke chapter 1, 2 and 3. Wooldridge part 1. Lecture notes.

WEEK 4
Inference and Hypothesis Testing.
Topics: 
•    What is a statistical test and how it is constructed 
•    The decision rule 
•    Type I and type II errors 
•    Power of a test
•    Finite sample and asymptotic tests in the context of a regression model
Larsen and Marx, chapters 6 and 9. Casella and Berger, chapter 8. Lecture notes

WEEK 5
Instrumental Variable Estimation
•    The traditional interpretation and the Angrist- Imbens-Rubin interpretation of IV 
•    Average Treatment Effect and Average Treatment Effect for the Treated
•    Local Average Treatment Effect
Woolridge (2009); Angrist and Pischke (2013). Lecture notes

Bibliography and further readings

Main References:
•    Richard J. Larsen and Morris L. Marx. An introduction to mathematical statistics and its applications. Prentice Hall, Fifth Edition, 2012.
•    George Casella and Roger L. Berger. Statistical Inference. Thomson, Second Edition, 2002.
•    Jeffrey Wooldridge, Introductory Econometrics. A Modern Appproach. South Western Cengage Learning, 2009
•    Joshua Angrist and Jorn-Steffen Pischke. Mostly Harmless Econometrics. An Em- piricist’s Companion. Princeton University Press, 2013.
•    Lecture notes by the instructor.


 

Register for this course