The main goal of this Core course is to give an introduction to the basic tools that an econo- metrician 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.
 

Topics

 

Topic 1

Introduction: what is econometrics about; the tool-box of econometrics; the econo- metrics sequence at the EUI; Content of this course.
Estimation: Estimators and estimates; the Method of maximum Likelihood; the Method of Moments.
Larsen and Marx, chapter 5. Casella and Berger, chapter 7 . Lecture notes.

Topic 2

Estimation: Finite sample properties of estimators; Unbiasedness, Efficiency,, Suffi- ciency, Minimum variance estimators; The Cramer-Rao Lower Bound, Invariance.
Larsen and Marx, chapter 5. Casella and Berger, chapter 7 and chapter 5. Lecture notes.

Topic 3

Estimation: Asymptotic properties of estimators; Asymptotic Unbiasedness, Asymp- totic Efficiency, Consistency; Asymptotic Normality
Basic asymptotics: concepts of convergence; Law of Large Numbers; Central Limit theorem; Continuous Mapping Theorem, Slutzky Theorem and Delta Method.
Larsen and Marx, chapter 5. Casella and Berger, chapter 7 and chapter 5. Lecture notes.

Topic 4

Simple regression: 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.
Angrist and Pischke chapter 1, 2 and 3. Wooldridge part 1. Lecture notes.

Topic 5

Simple regression: 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.

Topic 6

Simple regression: Causality and Regression.
Angrist and Pischke chapter 1, 2 and 3. Lecture notes.