# ECON 6910

# Applied Econometrics

Dr. Philip Shaw

Dealy Hall, East 522

Phone: 718-817-4048

Email: pshaw5@fordham.edu

Office Hours: Monday, 2pm-3:30pm & Thursday 5:30pm-7pm.

This
class will begin with an exploration of the properties required to
obtain causality in econometrics. We will focus largely on
the theoretical properties of conditional expectations operators and
basic asymptotic theory applied to ordinary least squares (OLS),
two-stage least squares (2SLS), and nonlinear methods such as discrete
response models. Although this is an applied class, I expect
students to have a good grasp on the theoretical properties of each
type of estimator covered. We will also introduce nonparametric
methods and contrast them to the parametric methods introduced in the
first half of the semester.

The grading for the class breaks down as follows:

Midterm (40%)

Final Exam (40%)

Class Project (15%)

Problem Sets (5%)Textbooks:

Wooldridge, J., 2010. Econometric Analysis of Cross Section and Panel Data, MIT Press, Edition 2.

Li, Q. and Racine, J., 2007. Nonparametric Econometrics: Theory and Practice, Princeton University Press, Edition 1.

Course Outline:

I. Introduction

1. Causal Relationships and Ceteris Paribus Analysis

2. Conditional Expectations and Related Concepts in Econometrics

3. Basic Asymptotic Theory

II. Linear Models

4. Single-Equation Linear Model and Ordinary Least Squares Estimation

5. Instrumental Variables Estimation of Single-Equation Linear Models

6. Additional Single-Equation Topics

7. System Estimation of Instrumental Variables and Simultaneous Equations Models

III. Nonlinear Models and Related Topics

8. Binary Response Models

IV. Nonparametric Methods

9. Density Estimation

10. Regression Estimation with Exogenous Covariates

11. Regression Estimation with Endogenous Covariates

Where to get R: http://cran.wustl.edu/

Nonparametric Econometrics in R

User Guide for NP Package in R

A Presentation on Nonparametrics in R

R-code Posted Below

R-Code for Moreira's CLR and IV Estimation

R-Code for Robust Standard Errors

## Problem Sets:

## Data Sets and Descriptions:

DOWNLOAD ENTIRE DATA SET BELOW:

INDIVIDUAL DATA SETS:

## Additional Readings Posted Below

Bootstrap Additional ReadingsHorowitz, J. “The Bootstrap”., Handbook of Econometrics, Volume 5, 2001. p. 3160-3186. Download Here!

Limited Dependent Variable Additional Readings

Hausman, J.A., Abrevaya, Jason, and Scott-Morton, F,M., 1998. "Misclassification of the Dependent Variable in a Discrete-response Setting"., Journal of Econometrics, Vol. 87 239-269. Download Here!

Lewbel, A., 2000. "Identification of the Binary Choice Model with Misclassification"., Econometric Theory, Vol. 16 (4), 603-609. Download Here!

Instrumental Variables Estimation Additional Readings

Staiger, Douglas and Stock, James H., 1997. "Instrumental Variables Regression with Weak Instruments"., Econometrica, Vol. 65 (3), 557-586. Download Here!

Moreira, Marcelo., 2003. "A Conditional Likelihood Ratio Test for Structural Models"., Econometrica, Vol. 71 (4), 1027-1048. Download Here!

Lewbel, Arthur., 2012. "Using Heteroscedasticity to Indentify and Estimate Mismeasured and Endogenous Regressor Models"., Journal of Business & Economic Statistics, Vol. 30 (1), 67-80. Download Here!