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.

For a smooth start, please follow these instructions for downloading and customizing these software components exactly.

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

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:
  1. R Material

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

  1. Lecture Notes
  1. R material

Additional Materials:

  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

Previous Exams

Midterms

Finals