DETAILED SYLLABUS
Quantitative methods in economics, finance and management
1. Information about the program
1.1 Higher education institution
1.2 Faculty
1.3 Department
1.4 Field of study
1.5 Study cycle
1.6 Specialization/Program of study
Babes-Bolyai University of Cluj-Napoca
Faculty of Economics and Business Administration
Economics and Business Administration for German line of study
Management
Master
International Business Management
2. Information about the discipline
2.1 Discipline title
Quantitative methods in economics, finance and management
Discipline code
EME0388
2.2 The holder of the course
Lecturer PhD Gabriela Brendea
activities
2.3 The holder of the seminar
Lecturer PhD Gabriela Brendea
activities
2.4 Year of study
I 2.5 Semester
I 2.6 Type of assessment
E
2.7 Discipline regime
Ob
3. Total time estimated (hours per semester of teaching)
3.1 Number of hours per week
3
From which: 3.2 course
1
3.3 seminar/laboratory
2
3.4 Total hours of curriculum
42 From which: 3.5 course
14
3.6 seminar/laboratory
28
Time distribution
Study after textbook, course support, bibliography and notes
Additional documentation in library, on specialized electronic platforms and on the field.
Preparing seminars/laboratories, essays, portfolios and reports.
Tutoring
Examinations
Others activities...................................
3.7 Total hours for individual
study
108
Hours
40
20
40
4
4
3.8 Total hours per semester
3.9 Number of credits
150
6
4. Preconditions (if necessary)
4.1 Of curriculum
4.2 Of skills
5. Conditions (if necessary)
5.1. For conducting the
course
5.2. For conducting
seminar/laboratory
Videoproiector
Computers, software SPSS, internet connection
6. Specific skills acquired
Profess
Collection, selection and interpretation of relevant information, using quantitative methods.
ional
Ability to use the statistical methods in order to substantiate the managerial decisions.
skills
Ability to work in complex teams for resource efficiency, and use the quantitative methods to identify
and understand the macroeconomic factors which influence the organization.
Transv Ability to work in diverse teams, to communicate and to assume a leadership role when necessary.
Self training professional skills to adapt to the dynamic organizational environment.
ersal
skills
7. Course objectives (arising from grid of specific skills acquired)
7.1 General objective of the
The course provides a foundation in multivariate statistical methods used for
discipline
economic data, as an important aid in effective and efficient planning.
7.2 Specific objectives
The main topics include descriptive statistics (frequency distributions and
graphs, measure of location and dispersion), bivariate analysis, tests and
measures of relationship between the variables, linear regression models,
logistic regression models, analysis of time series and predictions. The
purpose of the course is to prepare the students to be able to apply the
statistical methods and procedures in economics, finance or management.
Data analysis is done using SPSS or some other statistical packages. The
exams emphasize comprehension not computation.
8. Contents
8.1 Course
Teaching methods
Basis sources of data in statistics. Internal data sources. External
Lecturing+ Collaborating
data sources. Types of Sampling
Univariate analysis of data. Frequency distributions and graphs
Lecturing+ Collaborating
Measures of location
Lecturing+ Collaborating
Measures of dispersion and shape. Study of concentration
Lecturing+ Collaborating
Observations
1 lecture
1 lecture
1 lecture
1 lecture
The normal probability distribution. Hypothesis testing
Lecturing+ Collaborating
2 lectures
Bivariate analysis. The chi-square statistics and measures of
Lecturing+ Collaborating
1 lecture
association
Analysis of variance (ANOVA)
Lecturing+ Collaborating
1 lecture
Linear regression analysis
Lecturing+ Collaborating
1 lecture
Multiple linear regression. Nonlinear regression
Lecturing+ Collaborating
2 lectures
Introduction to time series analysis. Time series decomposition
Lecturing+ Collaborating
1 lecture
Seasonal component. Ratio-to-moving average method
Lecturing+ Collaborating
1 lecture
Autoregressive-moving average models
Lecturing+ Collaborating
1 lecture
Bibliography:
Kenkel, J.L. (1994), Introductory Statistics for Management and Economics, PWS Publishing Company, Boston,
U.S.A.
Griffiths, D. (2009), Head First Statistics.
Bowers, D. (1991) Statistics for Economics and Business, Macmillan Education Ltd, U.K.
Makridakis S., Wheelwright S.C., Hyndman R.J., Forecasting. Methods and Applications, John Wiley & Sons Inc.,
1998.
Mills, T.C., The econometric modelling of financial time series, Cambridge University Press, 1999.
8. 2 Seminar/laboratory
Teaching methods
Discussions
Introductory lecture in SPSS
Univariate analysis of data. Frequency distributions and graphs Problem solving+ Projects
Problem solving+ Projects
Measures of location, dispersion, shape
Problem solving+ Projects
The common probability distributions
Observations
1 laboratory
1 laboratory
2 laboratories
1 laboratory
Problem solving+ Projects
1 laboratory
Bivariate analysis. The chi-square statistics and measures of Problem solving+ Projects
association
Problem solving+ Projects
Analysis of variance (ANOVA)
1 laboratory
Linear regression analysis
Problem solving+ Projects
1 laboratory
Multiple linear regression. Nonlinear regression
Problem solving+ Projects
2 laboratories
Steps in testing hypothesis
1 laboratory
Introduction to time series analysis. Time series decomposition Problem solving+ Projects
1 laboratory
Seasonal component. Ratio-to-moving average method
Problem solving+ Projects
1 laboratory
Autoregressive-moving average models
Problem solving+ Projects
1 laboratory
Bibliography:
Kenkel, J.L. (1994), Introductory Statistics for Management and Economics, PWS Publishing
Company, Boston, U.S.A.
Griffiths, D. (2009), Head First Statistics.
Bowers, D. (1991) Statistics for Economics and Business, Macmillan Education Ltd, U.K.
Makridakis S., Wheelwright S.C., Hyndman R.J., Forecasting. Methods and Applications, John
Wiley & Sons Inc., 1998.
Mills, T.C., The econometric modelling of financial time series, Cambridge University Press, 1999.
9. Corroboration / validation of the discipline content according to the expectations of the epistemic
community representatives, of the ones of the professional associations and also of the representative
employers of the corresponding program.
The course content is continuous harmonized with that from the similar faculties from our country and abroad,
incorporating innovations in the field of curriculum and achieving a correlation with labor market requirements.
10. Evaluation
Type of activity
10.1 Evaluation criteria
10.2 Methods of assessment
10.4 Course
Acquiring the basic concepts and being able
to use them properly
Written exam
10.3 Share in final
grade
30%
10.5
The student has to be able to apply in
Written exam+Projects
35% Written exam
Seminar/laboratory practice the concepts and the statistical
35% Projects
techniques in order to analyze the real
economic data and to elaborate empirical
studies
10.6 Minimum standard of performance
The independent work proven by presenting the projects is a condition for the attendance at the final exam. The
evaluation focuses on comprehension, not on computation.
Date of completion
12.01.2014
Approval date by department
20.01.2014
Signature of the course holder
Lector univ. dr. Gabriela Brendea
Signature of the seminar holder
Lector univ. dr. Gabriela Brendea
Signature of the Head of the Department
Prof. univ. dr. Mariana Muresan