# Empirical Economics II

“AAEC / ECON 5126”

Welcome to our course web site. New material will be added as we move along. Anything below the “In Progress” line is subject to change and edits.

## Syllabus and General Announcements

1. Syllabus (pdf)
2. LaTeX material for syllabus
• main tex file (tex)
• bibliography (bib)
• table with schedule called by the main script (tex)
• excel file for table (xl)

## Software Instructions

We will be using R as our statistical programming package and LaTeX for word processing. The Sweave package combines the two to create unified documents that contain - subject to your full control - programming code, statistical results, tables, figures, comments, equations, and discussion. We will be using the RStudio interface to run R and to compose your Sweave files.

### Folder environment

1. Instructions (pdf)
2. Instructions (tex)

### Installing and customizing LaTeX for Windows

1. Instructions (pdf)
2. Instructions (tex)
3. testscript (tex)
4. testscript pdf (what the final product should look like) (pdf)

#### How to convert Excel tables into LaTeX and insert them into a LaTeX document (probably not needed for this course, but useful)

1. Instructions (pdf)
2. Instructions (tex)
3. Original Excel table (xl)
4. Tex version of a table after conversion (tex)

### Installing and custimizing R

1. Instructions (pdf)
2. Instructions (tex)

### Installing and customizing RStudio

1. Instructions (pdf)
2. Instructions (tex)

### Running Rstudio and R on VT’s Advanced Research Computing (ARC) cluster

1. Instructions (pdf)
2. Instructions (tex)

## Course Content

### Module 1: Classical Linear Regression and Least Squares

#### Overview of Estimation Frameworks and Estimators

1. Lecture Notes
1. R Material

#### CLRM and Least Squares

1. Lecture Notes
1. OLS slides
1. R Material
1. data

#### Finite Sample Properties of the OLS Estimator

1. Lecture Notes
1. R Material

### Module 2: Maximum Likelihood Estimation

1. Lecture Notes
1. R Material

### Module 3: Asymptotic Properties, Inference, Hypothesis Testing

#### Recap: Asymptotic Theory

1. Lecture Notes
1. R Material

#### Hypothesis Testing, Model Selection, and Prediction in Least Squares and ML Estimation

1. Lecture Notes
1. R Material
1. data

### Module 4: Instrumental Variables, Generalized Linear Regression

#### Omitted Variables, Instrumental Variables, and Two-Stage Least Squares

1. Lecture Notes
1. R Material
1. data

#### Generalized Least Squares, Robust Estimation, Heteroskedasticity

1. Lecture Notes
1. R Material
1. data

#### Generalized Least Squares and Serial Correlation

1. Lecture Notes
1. R Material
1. data

### Module 5: Estimation of Treatment Effects

#### Introduction

1. Lecture Notes

#### Estimation via Regression

1. Lecture Notes
1. R material
1. data

#### Estimation via Propensity Score

1. Lecture Notes
1. R material

#### Estimation via Matching

1. Lecture Notes
1. R material

#### Balance in Matching

• Simulated housing data example
• Prep script to generate graphs for housing data example
• Main script to generate graphs for housing data example
• Coastal flood zone presentation
• Mountain pine beetle paper (see e-mail)

### Module 6: Introduction to Bayesian Econometrics

#### Bayesian Econometrics: Introduction

1. Lecture Notes
1. QENov2017example

#### Normal Regression with Conjugate Priors

1. Lecture Notes
1. R Material

#### Normal Regression with Independent Priors

1. Lecture Notes:
• Conditional Posteriors
• Gibbs Sampler, Diagnostics, and Blocking
1. R Material

#### Posterior Predictive Densitites and p-Values, Highest Posterior Density Intervals

1. Lecture Notes
1. R material

1. Working with indicator (0/1) variables

## Problem Sets

1. PS1 - due Feb. 11
2. PS2 - due Feb. 27
3. PS3 - due Mar. 31
4. PS4 - due Apr. 9
5. PS5 - due Apr. 28
6. Final Exam - due May 12, 1 pm