Empirical Economics II

“AAEC / ECON 5126”

This site provides administrative instructions and sample lecture materials for the course “Empirical Economics,” the second module in the econometrics sequence for the PhD degree in Economics at Virginia Tech.

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)

Sample Course Content

Module 1: Classical Linear Regression and Least Squares

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