040639 UK Exact Tests not only for Experimental Economics (MA) (2017S)

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 served").

Anmeldung von Mi 15.02.2017 09:00 bis Mi 22.02.2017 12:00
Abmeldung bis Di 14.03.2017 23:59

Details

max. 50 Teilnehmer*innen

Sprache: Englisch

Lehrende

Karl Schlag

Termine (iCal) - nächster Termin ist mit N markiert

    
    Donnerstag
    02.03.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    09.03.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    16.03.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    23.03.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    30.03.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    06.04.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    27.04.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    04.05.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    11.05.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
Seminarraum 4  Oskar-Morgenstern-Platz 1  1.Stock
    
    Donnerstag
    11.05.
    11:20 - 13:00
    Seminarraum 4  Oskar-Morgenstern-Platz 1  1.Stock
    
    Donnerstag
    18.05.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    01.06.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    08.06.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    22.06.
    09:45 - 11:15
    Seminarraum 1 Oskar-Morgenstern-Platz 1  Erdgeschoß
    
    Donnerstag
    29.06.
    09:45 - 13:00
    Hörsaal 7  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

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 07.09.2020 15:29