Teaching




Summer 2018


040067/3 UK Applied Economics


Abstract
The course introduces the main workhorse of applied empirical research in economics, linear regression by ordinary least squares (OLS). After having taken the course, students should understand and be able to evaluate applied analysis of cross-section data and be able to undertake such analysis themselves. A major focus of the course is on historical data and cliometric research questions. Thus, the course is also relevant for students of economic history interested in quantitative methods. The main output shall be an independent research paper on a data set of own choice. Basic theoretical knowledge as well as computer skills are required.

Time: Wednesday, 06:30 PM - 08:00 PM, weekly, March 2018 - June 2018

Place: University of Vienna, Dept. of Economics, Oskar-Morgenstern-Platz 1, 1st basement floor, PC seminar room 5

Start: First meeting on the 7th of March 2018

Interaction: English is the language of instruction; Office hours on appointment; E-mail: holzner@wiiw.ac.at

Assessment: Test (20 points), participation in class (35 points) and an independent research paper (45 points) to be handed in in written form and to be presented at the end of the term. A minimum of 51 points is needed for a positive evaluation.

Outline: Introduction to econometrics and cliometrics; Review of probability and statistics; How to find and handle (historical) economic data; Linear regression with one regressor; Hypothesis testing; Linear regressions with multiple regressors; Introduction to the general-purpose statistical software package STATA; Nonlinear regression functions; Assessing statistical studies; Introduction to instrumental variable regressions; Estimation of popular economic models such as the Cobb-Douglas production function; Introduction to LaTeX; Presentation and discussion of the independent research papers.

Motto: There are two things you are better off not watching in the making: sausages and econometric estimates. (Edward Leamer)

Flyer: Course description

Lectures: Lectures and excercise documentation (password protected)



Mario Holzner’s CV and List of Publications; PhD thesis documentation; Back to the main page.