
Econometrics II

Professor: Ricardo Mora
STATA Sessions teacher: Yunrong Li
E-mail: ricmora@eco.uc3m.es
Phone: 91-624-9576
Office: 15.2.08
Office Hours: Wednesdays 14:00-16:00

Course Overview

This course is a follow-up of Econometrics I as an introduction to the econometric skills used in empirical economic research. You should gain an understanding and working knowledge of using econometric techniques for cross sections and panel data to conduct applied research in economics. The goal is to help you develop not only the ability to do empirical research in economics, but also the ability to critically read published research. This goal will be accomplished through classroom lectures, classroom practical sessions, and problem sets.

Course reference: Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach, 2nd Edition (zip)
The material for STATA sessions will be sent by email before each session.

Calendar, Syllabus,

Multiple Regression Analysis

Slides #1 Computer Practice #1

Inference

Slides #2 Computer Practice #2 Exercises Set #1 (Hand-in date: February 14th)

Heteroskedasticity and Serial Correlation

Slides #3 Computer Practice #3 Exercises Set #2: (Hand-in date: February 21st) (Durbin-Watson Tables)

Specification Problems

Slides #4 Exercises Set #3 (Hand-in date: February 28th)

Panel Data Methods

Slides #5 Computer Practice #4 Exercises Set #4 (Hand-in date: March 6th)

IV Estimation and 2SLS

Slides #6 Computer Practice #5 Exercises Set #5 (Hand in date: March 19th)

Course Contents

1. Multiple Regression Analysis:

The model with k independent variables. OLS estimation. Interpretation of the OLS regression equation. Properties of OLS residuals. The "partialling-out" interpretation. Comparison of simple and multiple regression estimates. Goodness of fit. Unbiasness. Omitted variable bias. Standard Errors of the OLS estimators. The Gauss-Markov Theorem. (Wooldridge, Chapters 3 and 5)

2. Inference:

The OLS Estimator under the Classic Assumptions. Consistency and asymptotic normality with large samples. The t test. Testing linear combinations of the parameters: the F test. The LM statistic. Asymptotic efficiency. (Wooldridge, Chapters 4 and 5)

3. Heteroskedasticity and Serial Correlation:

Consequences of heteroskedasticity for OLS. Heteroskedasticity-Robust Inference. Testing for Heteroskedasticity. Weighted Least Squares Estimation. Serial Correlation. Testing for serial correlation. Heteroskedasticity in Time Series Regressions. (Wooldridge, Chapter 8 and 12)

4. Specification Problems:

Functional form misspecification. Using proxy variables. Measurement error. Missing data and non-random sampling. (Wooldridge, Chapter 9)

5. Panel Data Methods:

Pooling independent cross sections across time. Two-period panel data. Policy analysis with 2-period panel data. Fixed effects estimation. Random effects models. Applying panel data methods to other data structures. (Wooldridge, Chapters 13 and 14)

6. IV Estimation and 2SLS:

Motivation. IV estimation of the MR model.2SLS. IV solutions to errors-in-variables problems. Testing endogeneity and overidentifying restrictions. 2SLS with heteroskedasticity. Applying 2SLS to panel data. (Wooldridge, Chapter 15)

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