Syllabus Topics 2018- 2019

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SYLLABUS
1. Information about the Study Programme
1.1 Higher Education Institution
UNIVERSITY OF BUCHAREST
1.2 Faculty/Department
ADMINISTRATION AND BUSINESS & PSYCHOLOGY AND
EDUCATIONAL SCIENCES
1.3 Chair
DEPARTMENT OF ECONOMIC AND ADMINISTRATIVE SCIENCES
1.4 Study Domain
BUSINESS ADMINISTRATION & PSYCHOLOGY
1.5 Study Level
MASTER
1.6 Study Programme /Qualification
BEHAVIORAL ECONOMICS
2. Information about the Course
2.1 Course Title
Topics in Econometrics for Behavioral Economics Research
2.2 Course Instructor
Professor PhD. Elena Druică
2.3 Seminar Instructor
2.4 Year of study
Professor PhD. Elena Druică
II
2.5 Semester
I
2.6 Type of evaluation
E
2.7 Type of course
Oblig.
3. Total estimated time (no. of hours per semester for the teaching activities)
3.1 No. of hours per week
4
out of which: 3.2 course
2
3.3 seminar/laboratory
2
3.4 Total no. of hours in the
curriculum
56
out of which: 3.5 course
14
3.6 seminar/laboratory
28
Time distribution
Hours
Study on written course material, bibliography and notes
30
Supplementary research in the library, on specialized electronic platforms and on site
26
Preparation for seminars /laboratories, tasks, essays, portfolios and reviews
30
Tutoring
5
Evaluations
3
Other activities..................................................
-
3.7 Total hours of individual study
94
3.9 Total hours per semester
150
3.10 No. of credits
6
4. Prerequisites (where appropriate/applicable)
4.1 Curriculum prerequisites
Parametric statistics; Nonparametric statistics.
4.2 Prerequisite skills /competencies
Basic R skills
5. Conditions (where appropriate/applicable)
5.1 Conditions for the course
Video projector, White board
5.2 Conditions for the seminar / laboratory
White board; R and R-Studio; JASP
6. Specific skills gained
Professional skills / competencies
C1. The advanced use of the behavioral economics’ concepts and principles, for
consultancy and counseling in different processes of strategic decision-making
2/6
C2. Data collection, data filtering and data analysis: 2/6
C5. Offering specialized consultancy for decision-making optimization: 1/6
Transversal skills / competencies
CT3. Assessment and diagnosis of the continuous learning needs, in order to
efficiently adapt to the dynamic of the socio-economic environment: 1/6
7. Course objectives (based on the specific skills gained)
7.1 General objective of the course
To present advanced econometric tools applied in the Behavioral Economics
research
7.2 Specific objectives
To review the basis of econometric modeling
To introduce the main models used in working with cross – sectional data
To provide a brief introduction to time series econometrics
8. Syllabus (course outline)
8.1 Course
Teaching methods
Observatio
ns
1. Introduction.
1.1. Requirements.
1.2. The Simpson’s Paradox and the need for Econometrics
1.3. Brief review of the linear regression model
2. Mediation analysis:
Lectures, Audiovisual techniques,
2.1. The simplest mediation model in R. Concept, method and applications.
numerical applications, case
2.2. Example: The presumed media influence theory
studies, experiments
2.3. Applications in R.
3. From mediation to moderation
3.1. Introducing the idea of moderation
3.2. Example: joint ventures and economic performance (Gong, T. Y., Shenkar,
O., Luo, Y., & Nyaw, M.-K. (2007). Do multiple partners help or hinder
international joint venture performance?: The mediating roles of contract
completeness and partner cooperation. Strategic Management Journal, 28,
1021–1034. )
4. Working with both moderation and mediation
4.1. Economic stress and professional network support
Pollack J, VanEpps E. and Hayes A. (2012) The moderating role of social ties on
entrepreneurs’ depressed affect and withdrawal intentions in response to
economic stress, Journal of Organizational Behavior, 33, 789–810
5. Models with multiple mediators
5.1. The need for multiple mediators
5.2. Back to presumed media influence and economic stress to illustrate the
models.
6. Logistic regression in R
Lectures, Audiovisual techniques,
6.1. A linear probability model
numerical applications, case
6.2. The logit model
studies, experiments
6.3. Getting into graduate school.
7. Practice the logistic regression.
Lectures, Audiovisual techniques,
7.1. An application to real data
numerical applications, case
7.2. Working with train and test set; developing accurate predictions
studies, experiments
8. Endogeneity – Part 1.
Lectures, Audiovisual techniques,
8.1. Introductory considerations
numerical applications, case
8.2. How to detect endogeneity
studies, experiments
8.3. Working on the education - income relationship
9. Endogeneity – Part 2.
Lectures, Audiovisual techniques,
9.1. How to deal with endogeneity
numerical applications, case
9.2. Two-stage regression
studies, experiments
10. Endogeneity in research papers.
Lectures, Audiovisual techniques,
10.1. Endogeneity in developmental studies
numerical applications, case
10.2. Endogeneity in education and income
studies, experiments
11. Segmentation analysis – Part I
Lectures, Audiovisual techniques,
11.1. The basic idea of cluster analysis
numerical applications, case
11.2. Hard clustering (hierarchical clustering, k – means clustering)
studies, experiments
11.3. Applications
11. Segmentation analysis – Part II
11.1. Fuzzy clustering
11.2. Applications
13. Classification trees and decision making
Lectures, Audiovisual techniques,
13.1. The idea behind classification trees
numerical applications, case
13.2. A simple example
studies, experiments
13.3. Random forest and reliability
14. A final lecture including a presentation of a research project report that
Lectures, Audiovisual techniques,
involves all the studied topics.
numerical applications, case
studies, experiments
Bibliography

Lecture notes – Professor PhD. Elena Druică

R – codes provided by Professor PhD. Elena Druică

R
Software
and
the
R
–
Studio
interface,
freely
available
at
http://www.r-project.org/
https://www.rstudio.com/about/ respectively

Gujarati, D. N. (1995). Basic Econometrics, 3rd edition, New York: McGraw-Hill, or later, freely available online
and

Gujarati. D. N (2011). Econometrics by Example, New York: McGraw-Hill, freely available online

Hayes A. (2013) Introduction to Mediation, Moderation, and Conditional Process Analysis,A Regression-Based
Approach, Guilford Press
8.2 Seminar/laboratory
Teaching methods
Observatio
ns
1. Starting the work:
Discussions of research papers; R
1.1. Discuss the possible topic for your final project.
coding.
1.2. Identify the research question, the variables and the measurement
methods
2. Introduction to mediation analysis
Numerical applications, case
Paper to be discussed: Tal-Or, N., Cohen, J., Tsafati, Y., & Gunther, A. C. (2010). studies, discussions of research
Testing causal direction in the influence of presumed media influence. papers
Communication Research, 37, 801-824.
3. Introduction to moderation analysis
Paper to be discussed: Gong, T. Y., Shenkar, O., Luo, Y., & Nyaw, M.-K. (2007).
Do multiple partners help or hinder international joint venture performance?:
The mediating roles of contract completeness and partner cooperation.
Strategic Management Journal, 28, 1021–1034.
4. Working with both moderation and mediation
4.1. Economic stress and professional network support
Pollack J, VanEpps E. and Hayes A. (2012) The moderating role of social ties on
entrepreneurs’ depressed affect and withdrawal intentions in response to
economic stress, Journal of Organizational Behavior, 33, 789–810
5. Models with multiple mediators
5.1. The need for multiple mediators
5.2. Back to presumed media influence and economic stress to illustrate the
models.
6. Logistic regression in R
6.1. A linear probability model
6.2. The logit model
6.3. Getting into graduate school.
7. Practice the logistic regression.
Numerical applications, case
7.1. An application to real data
studies, discussions of research
7.2. Working with train and test set; developing accurate predictions
papers
8. Endogeneity – Part 1.
Numerical applications, case
8.1. Introductory considerations
studies, discussions of research
8.2. How to detect endogeneity
papers
8.3. Working on the education - income relationship
9. Endogeneity – Part 2.
Numerical applications, case
9.1. How to deal with endogeneity
studies, discussions of research
9.2. Two-stage regression
papers
10. Endogeneity in research papers.
Papers presentation
10.1. Endogeneity in developmental studies
10.2. Endogeneity in education and income
11. Segmentation analysis – Part I
Numerical applications, case
11.1. The basic idea of cluster analysis
studies, discussions of research
11.2. Hard clustering (hierarchical clustering, k – means clustering)
papers
11.3. Applications
11. Segmentation analysis – Part II
Numerical applications, case
11.1. Fuzzy clustering
studies, discussions of research
11.2. Applications
papers
13. Classification trees and decision making
Numerical applications, case
13.1. The idea behind classification trees
studies, discussions of research
13.2. A simple example
papers
13.3. Random forest and reliability
14. Brief presentations of the main results students found in their own projects. In preparation for the final exam, this
seminar will be devoted to discussions regarding students’ projects structure and content.
9. Correlation of the syllabus with the expectations of the representatives of epistemic
community, of professional associations, employers, which are representative for the domain
of the study programme
The syllabus provides the core tools for econometric modeling of cross – sectional data. The toolkit taught during this class will
help a behavioral economist to implement a wide range of applied studies and to assess the effectiveness of various policies.
10. Evaluation
Type of activity
10.1 Evaluation criteria
Project presentation
10.4 Course
10.2 Evaluation methods
Research project (Model, final 60%
conclusions, implications and
limitations).
Default
Midterm evaluation
10.5 Seminar/laboratory
Research project (theoretical
background and descriptive
statistics)
10.6 Minimum standards: 50%
Date of completion
1. 10.2018
10.3 Percentage of the final
grade
Signature of the Course Instructor
Professor PhD. Elena Druică
10%
30%
Date of approval in Department
.........................
Signature of the Head of the Department
Conf.univ.dr. Anca Bratu
.........................
ADDENDUM TO SUBJECT CARD
b. Evaluation – Grade increases
Type of activity
10.4 Course
10.1 Evaluation criteria
Project presentation
Default
Midterm evaluation
10.5 Seminar/laboratory
10.2 Evaluation methods
Research project
10.3 Percentage of the final
grade
60%
10%
Multiple choice questions and
coding in R.
30%
10.6 Minimum standards: 50%
Depending on the grade that is to be increased.
Date of completion
1.10.2017
Signature of the Course Instructor
Professor PhD. Elena Druică
Date of approval in Department
Signature of the Head of the Department
Conf.univ.dr. Anca Bratu
.........................
.........................
c. Evaluation – Make-up grades
Type of activity
10.4 Course
10.5 Seminar/laboratory
10.1 Evaluation criteria
Project presentation
10.2 Evaluation methods
Research project
Default
Midterm evaluation
Multiple choice questions and
coding in R.
10.3 Percentage of the final
grade
60%
10%
30%
10.6 Minimum standards
Date of completion
1.10.2017
Date of approval in Department
.........................
Signature of the Course Instructor
Professor PhD. Elena Druică
Signature of the Head of the Department
Conf.univ.dr. Anca Bratu
.........................
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