040067/3 UK Applied Economics
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 2019 - June 2019
Place: University of Vienna, Dept. of Economics, Oskar-Morgenstern-Platz 1, 1st basement floor, PC seminar room 5
Start: First meeting on the 6th of March 2019
Interaction: English is the language of instruction; Office hours on appointment; E-mail: firstname.lastname@example.org
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;
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