Universität Wien FIND

Bedingt durch die COVID-19-Pandemie können kurzfristige Änderungen bei Lehrveranstaltungen und Prüfungen (z.B. Absage von Vor-Ort-Lehre und Umstellung auf Online-Prüfungen) erforderlich sein. Melden Sie sich für Lehrveranstaltungen/Prüfungen über u:space an, informieren Sie sich über den aktuellen Stand auf u:find und auf der Lernplattform moodle. ACHTUNG: Lehrveranstaltungen, bei denen zumindest eine Einheit vor Ort stattfindet, werden in u:find momentan mit "vor Ort" gekennzeichnet.

Regelungen zum Lehrbetrieb vor Ort inkl. Eintrittstests finden Sie unter https://studieren.univie.ac.at/info.

Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.

040639 UK Exact Tests not only for Experimental Economics (MA) (2013W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first serve").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Montag 07.10. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 14.10. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 21.10. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 28.10. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 04.11. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 11.11. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 18.11. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 25.11. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 02.12. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 09.12. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 16.12. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 13.01. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag 20.01. 12:00 - 14:00 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Dienstag 28.01. 14:00 - 16:00 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This course is about understanding what can go wrong big time when relying on asymptotic
theory and understanding which approaches do what they say they do. Exact testing refers
to methods do exactly this, they have properties that can be formally proven. Claims that
are not based on a handful of simulations when the underlying set of possible data
generating processes is so rich that one can never simulate many.

Art der Leistungskontrolle und erlaubte Hilfsmittel

The grade is made up of a) a midterm, b) a final and c) homeworks that involve finding
data sets, and analyzing data sets. Each of these three parts will be separately graded and
counts equally towards the final grade.
Prerequisites: knowledge of statistics at an undergraduate level.

Mindestanforderungen und Beurteilungsmaßstab

In this course we will give an overview and understand of existing and new methods for
testing hypotheses and running regressions that are exact. One goal of this course is to teach
students how to use R in order to analyze data sets. Laptops will be used in class to
demonstrate methods. Students will learn how to analyze data sets and how to read and
understand empirical papers.

Who is this course for? Anyone who is curious and
who is genuinely interested in uncovering what is hidden in the data and who is interested
in making mathematically sound claims. Of course many applications cannot be dealt (yet)
with an exact method as often there is too much going on. However this course will
demonstrate that there are lots of relevant areas where one can make exact statements,
including running linear regressions.

Prüfungsstoff

Statistics is a science about how to analyze data. Classical statistical methods often, in fact
most statistical methods typically make claims about data sets that are not in accordance
with the underlying theory and methodology. This is because they make claims about
significance that are based on assuming that the data is infinitely large (they are based on
asymptotic theory). Remember that typically we do not think that the data is normally
distributed, but that is approximately and we will talk about why this sort of approximation
is not what one needs.

Literatur

Overview of Material:
Basics: Concepts: Null and alternative hypothesis, type I and II error, level, size, power, pvalue,
confidence interval. Data types: Single sample, independent samples, matched pairs,
Discussion of the usefulness of normality for large data sets.
Existing tests:
Binomial test with and without assuming identically distributed data, sign test, confidence
interval for the median and other quantiles, confidence interval of distributions,
permutation tests including Wilcoxon Mann Whitney and spearman rank correlation test.
New tests:
Test for the mean of a single sample, for comparing means given two independent samples
and for matched pairs, for the variance of a single sample and for comparing variances and
for analyzing covariance, for investigating tendencies described by a stochastic inequality,
and for running linear and ordinal regressions.
Reading material
Motulsky, Harvey (1995) "Intuitive Biostatistics," Oxford: Oxford Univ. Press.
- a bit vague but precise
Lehmann and Romano (2005) Lehmann, E. L. and Romano, J. P. (2005), Testing Statistical
Hypotheses. New York: Springer.
- very precise but too mathematical for the applied
Schlag (2013): Exact Hypothesis Testing without Assumptions - New and Old Results
not only for Experimental Game Theory
http://homepage.univie.ac.at/karl.schlag/research/statistics/exacthypothesistesting.pdf
and then there are original papers that some lectures will be based on:
Gossner and Schlag (2013): Finite-sample exact tests for linear regressions with bounded
dependent variables, Journal of Econometrics 177, 75-84.
Hoeffding, W. (1956), "On the distribution of the number of successes in independent
trials." The Annals of Mathematical Statistics, 27, 713-721.
Massart, P. (1990), "The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality," The
Annals of Probability 18, 1269-1283.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 07.09.2020 15:29