Discrete Choice Analysis 2400-M1IiEAWD

The aim of the course is to familiarize participants with the formal methods of describing discrete choice models, and current research achievements in this field.
[1] Review of discrete election models and their applications
[2] Introduction to discrete choices, the concept of discrete choices, a set of available choices
[3-4] The economic basis of discrete choices, linking economic and mathematical models through the interpretation of the utility function
[5-7] Discussion of various models of discrete choices, methods of their estimation, results interpretation, and verification of assumptions.
[8-15] Discussion on different applications of discrete choice models in different areas of economics (transport, banking, marketing, insurance (etc.). In the second part of the course, participants will present the results of empirical work chosen from recent literature or own.

Type of course

optional courses

Prerequisites (description)

Please give exact or approximate titles of other courses a student should have previously taken and passed in order to participate in the course described herein. Please describe the range of background knowledge/ skills/ other competences, expected from a student upon their enrolment in the course. Define the expected learning outcomes upon the course commencement. It is advisable to indicate other courses offered at the University of Warsaw of which completion, that is the achievement of defined learning outcomes, may facilitate the study of the course described herein.

Course coordinators

Paweł Strawiński

Learning outcomes

Po ukończeniu kursu uczestnik:

WIEDZA

Zna metody ilościowe i narzędzia opisu zjawisk o charakterze wyborów dyskretnych Zna źródła pozyskiwania danych ekonomicznych oraz posiada podstawowe informacje dotyczące metod postępowania w przypadku modelowania wyborów o charakterze dyskretnym. Zna sposoby wykorzystania pakietu Stata w opisie zjawisk ekonomicznych i społecznych. S2A_W06
Potrafi rozpoznać problem ekonomiczny z kategorii wyboru dyskretnego S2A_W06.

UMIEJĘTNOŚCI

Potrafi budować zaawansowane modele dla zjawisk ekonomicznych i społecznych o naturze wyboru dyskretnego, oraz oceniać rezultaty modele opisywanych w literaturze przedmiotu w sposób krytyczny S2A_U04, S2A_U07
Potrafi dokonać prezentacji wyników i napisać raport z przeprowadzonego badania empirycznego S2A_U09, S2AU_10.

KOMPETENCJE SPOŁECZNE

Znajomość podstawowymi funkcji pakietu statystycznego pozwala na rozszerzenie wiedzy we własnym zakresie. S2A_K01
Na podstawie przedstawionych interpretacji uzyskanych wyników potrafi być krytyczny w stosunku do przedstawionych modeli i prawidłowo identyfikuje i rozstrzyga dylematy wykorzystaniem tych metod w prowadzeniu własnej firmy lub pracy zawodowej S2A_K04, S2A_K07.
SU05, SU06, SK01, SK03, SU04, SU03, SU02, SU01, SW03, SW02, SW01, SW04, SW05, SK02, SK04

Assessment criteria

Detailed rules regarding class attendance result directly from the University of Warsaw study regulations, absences not exceeding 20% of the nominal class hours are allowed. Grade for the classes will be issued on the basis of the student's independent work or group work.
The student is obliged to be present during classes. According to the provisions of paragraph 33 of the University of Warsaw Studies Regulations, students absent from classes send a request to the teacher to justify their absence without undue delay. Absences exceeding 3 are not excused and result in a student's lack of classification.

Grade for the classes will be issued on the basis of the student's independent work or group work. The form and settlement dates of the final assignment will be agreed with the participants during the first meeting. An additional condition is the timely delivery of the final assessment.

Bibliography

Books:

Baum Kit (2006) "An Introduction to Modern Econometrics Using Stata", Stata Press David

Cameron Colin, Trivedi, Parvin K.. (2009) Microeconometrics Using Stata, Stata Press.

Hensher, John Rose, William Greene (2005) "Applied Choice Analysis. A Primer", CUP
Kenneth Train "Discrete Choice Methods with Simulations"

Long Scott J., Freese Jeremy (2003) Regression Models for Categorical and Limited Dependent Variables Using Stata, Revised Edition, Stata Press

Maddala G.S. (1999) Limited Dependent and Qualitative Variables in Econometrics, Cambridge University Press

William H. Greene (2003) Econometric Analisys, Pentrice Halls

Articles:

Ai, Chunrong i Norton Edward. (2003) Interaction terms in logit and probit models, Economic Letters, vol. 80, pp. 123-129.

Buis, Maarten. (2010) Stata tip 87: Interpretation of interactions in nonlinear models, Stata Journal, vol 10, Number 2, pp. 305-310.

Cameron Colin. i Windmeijer, F.A.G. (1993) R-Squared Measures for Count Data Regression Models with Applications to Health Care Utilization, Dept. of Economics Working Paper 93-24, University of California at Davis.

Mroz Thomas. (1987) The Sensivity of an Empirical Model of Married Women’s Hours of Work to Economic and Statistical Assumptions, Econometrica, vol 55(4), pp. 765-799.

Veall, Michael R. i Zimmermann, Klaus F. (1996) Pseudo-R2 Measures for Some Common Limited Dependent Variable Models . Collaborative Research Center 386, Discussion Paper 18.

Williams Richard (2011) Comparing Logit and Probit Coefficients Between Models and Across Groups

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 2400-M1IiEAWD in USOSweb