Econometrics - BA II 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
FACULTY OF BUSINESS AND ADMINISTRATION
1.3 Chair
DEPARTMENT OF ECONOMIC AND ADMINISTRATIVE SCIENCES
1.4 Study Domain
BUSINESS ADMINISTRATION
1.5 Study Level
BACHELOR
1.6 Study Programme /Qualification
BUSINESS ADMINISTRATION
2. Information about the Course
2.1 Course Title
Econometrics
2.2 Course Instructor
Professor PhD. Elena Druică
2.3 Seminar Instructor
PhD. Cosmin Imbrișcă
2.4 Year of study
2
2.5 Semester
1
2.6 Type of evaluation
E
2.7 Type of course
M
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
28
3.6 seminar/laboratory
28
Time distribution
Hours
Study on written course material, bibliography and notes
20
Supplementary research in the library, on specialized electronic platforms and on site
15
Preparation for seminars /laboratories, tasks, essays, portfolios and reviews
12
Tutoring
5
Evaluations
2
Other activities..................................................
15
3.7 Total hours of individual study
69
3.9 Total hours per semester
125
3.10 No. of credits
5
4. Prerequisites (where appropriate/applicable)
4.1 Curriculum prerequisites
Mathematics, statistics, microeconomics, macroeconomics
4.2 Prerequisite skills /competencies
Basic training in mathematics, statistics and economics
5. Conditions (where appropriate/applicable)
5.1 Conditions for the course
White board, video projector, computer with R software installed
5.2 Conditions for the seminar / laboratory
White board, computers with R software installed
6. Specific skills gained
Professional skills / competencies
C1. Gathering and analysing information regarding the interaction between the
firm and the environment, 1/5
C5. Using data sets speficif to business administration, 4/ 5
Transversale skills / competencies
7. Course objectives (based on the specific skills gained grill)
7.1 General objective of the course
7.2 Specific objectives
The objective of the course is to introduce students to the fundamental concepts
of Econometric theory and practice – the methodology used by economists, to
measure, evaluate, and forecast economic phenomena.




Understand the basic principles of statistical methods
Understand the nature of regression analysis
Be able to estimate a two-variable regression using R
Be able to correctly setup and justify a multiple regression for a typical
economic problem
 Understand the basic concept underlying hypothesis testing and statistical
inference
 Be able to interpret regression analysis output from R and draw statistical
inference for a typical economic problem
8. Syllabus (course outline)
8.1 Course
Teaching methods
Observations
1. Why Econometrics?
1.1. When mean fails to be very useful: the need for relations
between variables
1.2. Types of questions Econometrics can answer
1.3. Data used in Econometrics
1.4. Sources of data
1.5. Principles that underline the simple regression linear
model. Regression versus correlation.
1.6. What linearity in parameters actually means
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
-
2.
2.1.
2.2.
2.3.
2.4.
2.5.
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
-
3.
3.1.
3.2.
3.3.
Diagnostic of the simple linear regression model
The assumptions of the linear regression model
When the assumptions are violated
OLS: the accuracy of the estimation. The coefficient of
determination.
3.4. Gauss Markov Theorem
3.5. CLRM versus CNLRM
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
-
4. Testing hypothesis and confidence intervals in simple linear
regression
4.1. Confidence intervals for coefficients estimators
4.2. Testing the statistical significance of the coefficients
4.3. Regression versus ANOVA
4.4. Prediction
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
-
5. Extending the simple linear regression model
5.1. Regression through origin
5.2. Regression with standardized variables
5.3. Functional forms of the simple linear regression model: log –
log, log – linear and linear – log.
5.4. Reciprocal models
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
-
6. Multiple regression analysis
6.1. Building the model and interpreting the coefficients.
6.2. Multiple coefficient of determination, and correlation.
Lectures, Audiovisual
techniques, numerical
applications, case studies,
-
The simple linear regression model.
The population regression function.
Error term: meaning, sources and consequences
Sample estimated regression line.
OLS for estimating the coefficients of the model
Interpreting the estimated coefficients of the model
6.3. Misspecification of the model. The adjusted R – squared.
6.4. The Cobb – Douglas production function in Econometrics.
experiments
7. Other functional forms
7.1. Polynomial regression
7.2. Partial correlation coefficient
7.3. Applications
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
-
8. Specific tests
8.1. Testing the statistical significance of the individual
coefficients
8.2. Testing the overall statistical significance of the model
8.3. Testing the difference between coefficients of two different
regression models
8.4. Restricted OLS
8.5. The Chow test
8.6. Choosing the functional form of the model
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
-
9. Regression with dummy variables
9.1. Dummy variables. General considerations.
9.2. ANOVA Models
9.3. ANCOVA Models
9.4. Models with interactions
9.5. Dummy variables in seasonality analysis
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
10. Special aspects in regression with dummy variables
10.1. Piecewise regression
10.2. Dummy variables and heteroscedasticity
10.3. Dummy variables and autocorrelation
10.4. Interpret dummy variables in models with logarithm
10.5. When the dependent variable is a dummy variable
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
11. Multicollinearity: Effects
11.1. Multicollinearity as a characteristic of the data.
11.2. Effects of multicollinearity on estimated coefficients
11.3. How to detect multicollinearity and how to remedy it
11.4. Principal component analysis (PCA)
11.5. Limits in interpreting the PCA.
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
12. Heteroscedasticity: effects
12.1. Heteroscedasticity explained
12.2. Effects of heteroscedasticity on coefficients
12.3. How to detect Heteroscedasticity and how to remedy it
12.4. Applications, discussions and interpretations
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
13. Effects of the autocorrelations in errors
12.1. Autocorrelation explained
12.2. Effects of autocorrelation on coefficients
12.3. How to detect autocorrelation and how to remedy it
12.4. Applications, discussions and interpretations
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
14. Econometric modeling
14.1. Model specification
14.2. Effects of misspecification
14.3. Testing specification errors
14.4. Criteria for model specification.
14.5. An application
Lectures, Audiovisual
techniques, numerical
applications, case studies,
experiments
Bibliografie





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
Gujarati. D. N (2011). Econometrics by Example, New York: McGraw-Hill, freely available online
and
8.2 Seminar/laboratory
Teaching methods
Observations
1. Why Econometrics?
Numerical applications, case
1.1. When mean fails to be very useful: the need for relations
studies, experiments
between variables
1.2. Types of questions Econometrics can answer
1.3. Data used in Econometrics
1.4. Sources of data
1.5. Principles that underline the simple regression linear model.
1.6. Regression versus correlation.
1.6. What linearity in parameters actually means
-
2. The simple linear regression model.
2.1. The population regression function.
2.2. Error term: meaning, sources and consequences
2.3. Sample estimated regression line.
2.4. OLS for estimating the coefficients of the model
Interpreting the estimated coefficients of the model
Numerical applications, case
studies, experiments
-
3. Diagnostic of the simple linear regression model
3.1. The assumptions of the linear regression model
3.2. When the assumptions are violated
3.3. OLS: the accuracy of the estimation. The coefficient of
determination.
3.4. Gauss Markov Theorem
3.5. CLRM versus CNLRM
Numerical applications, case
studies, experiments
-
4. Testing hypothesis and confidence intervals in simple linear Numerical applications, case
regression
studies, experiments
4.1. Confidence intervals for coefficients estimators
4.2. Testing the statistical significance of the coefficients
4.3. Regression versus ANOVA
4.4. Prediction
-
5. Extending the simple linear regression model
Numerical applications, case
5.1. Regression through origin
studies, experiments
5.2. Regression with standardized variables
5.3. Functional forms of the simple linear regression model: log –
log, log – linear and linear – log.
5.4. Reciprocal models
-
6. Multiple regression analysis
6.1. Building the model and interpreting the coefficients.
6.2. Multiple coefficient of determination, and correlation.
6.3. Misspecification of the model. The adjusted R – squared.
6.4. The Cobb – Douglas production function in Econometrics.
Numerical applications, case
studies, experiments
-
7. Other functional forms
7.1. Polynomial regression
7.2. Partial correlation coefficient
7.3. Applications
Numerical applications, case
studies, experiments
-
8. Specific tests
Numerical applications, case
8.1. Testing the statistical significance of the individual
studies, experiments
coefficients
8.2. Testing the overall statistical significance of the model
8.3. Testing the difference between coefficients of two different
regression models
8.4. Restricted OLS
8.5. The Chow test
8.6. Choosing the functional form of the model
-
9. Regression with dummy variables
9.1. Dummy variables. General considerations.
9.2. ANOVA Models
9.3. ANCOVA Models
9.4. Models with interactions
9.5. Dummy variables in seasonality analysis
Numerical applications, case
studies, experiments
10. Special aspects in regression with dummy variables
10.1. Picewise regression
Numerical applications, case
10.2. Dummy variables and heteroscedasticity
10.3. Dummy variables and autocorrelation
10.4. Interpret dummy variables in models with logarithm
10.5. When the dependent variable is a dummy variable
studies, experiments
11. Multicollinearity: Effects
11.1. Multicollinearity as a characteristic of the data.
11.2. Effects of multicollinearity on estimated coefficients
11.3. How to detect multicollinearity and how to remedy it
11.4. Principal component analysis (PCA)
11.5. Limits in interpreting the PCA.
Numerical applications, case
studies, experiments
12. Heteroscedasticity: effects
12.1. Heteroscedasticity explained
12.2. Effects of heteroscedasticity on coefficients
12.3. How to detect Heteroscedasticity and how to remedy it
12.4. Applications, discussions and interpretations
Numerical applications, case
studies, experiments
13. Effects of the autocorrelations in errors
12.1. Autocorrelation explained
12.2. Effects of autocorrelation on coefficients
12.3. How to detect autocorrelation and how to remedy it
12.4. Applications, discussions and interpretations
Numerical applications, case
studies, experiments
14. Econometric modeling
14.1. Model specification
14.2. Effects of misspecification
14.3. Testing specification errors
14.4. Criteria for model specification.
14.5. An application
Numerical applications, case
studies, experiments
Bibliografie




R – codes provided by Professor PhD. Elena Druică
R Software and the R – Studio interface, freely available at http://www.r-project.org/ and
https://www.rstudio.com/about/ respectively
Selected example from Gujatari D. N (2008), Basic Econometrics, 5th edition, New York: McGraw-Hill: datasets freely
available online at http://www.econometrics.com/comdata/gujarati/
Gujarati. D. N (2011). Econometrics by Example, New York: McGraw-Hill, freely available online and the corresponding
example available at https://he.palgrave.com/companion/Gujarati-Econometrics-By-Example/student-zone/
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 course content is consistent with what is done in other universities in the country and abroad.
10. Evaluation
Type of activity
10.4 Course
10.1 Evaluation criteria
10.2 Evaluation methods
Academic honesty
Final written examination
Scientific validity of answers (problems and theoretical
questions)
Default
10.5 Seminar/laboratory
Academic honesty
Midterm examination
Scientific validity of answers (problems and theoretical
questions)
Classroom participation
10.3 Percentage of the final
grade
60%
10%
30%
Project presentation and
other homework
10.6 Minimum standards
30% of final examination and 15% of seminar activities
Date of completion
Signature of the Course Instructor
Professor PhD. Elena Druică
01.10.2018
Signature of the Seminar Lecturer
PhD. Cosmin Imbrișcă
-------------------------------------.........................
Signature of the Head of the Department
Assoc. Professor PhD. Anca Bratu
.........................
Date of approval in Department
.........................
ADDENDUM TO SUBJECT CARD
b. Evaluation – Grade increases
Type of activity
10.4 Course
10.1 Evaluation criteria
10.2 Evaluation methods
Academic honesty
Final written examination
Scientific validity of answers (problems and theoretical
questions)
Default
10.5 Seminar/laboratory
10.3 Percentage of the final
grade
60%
10%
Academic honesty
Midterm examination
Scientific validity of answers (problems and theoretical
questions)
Classroom participation
Project presentation and
other homework
30%
10.6 Minimum standards
30% of final examination and 15% of seminar activities
Date of completion
Signature of the Course Instructor
Signature of the Seminar Lecturer
01.10.2018
Date of approval in Department
.........................
.........................
Signature of the Head of the Department
.........................
.........................
c. Evaluation – Make-up grades
Type of activity
10.4 Course
10.1 Evaluation criteria
10.2 Evaluation methods
Academic honesty
Final written examination
Scientific validity of answers (problems and theoretical
questions)
Default
10.3 Percentage of the final
grade
60%
10%
10.5 Seminar/laboratory
Academic honesty
Midterm examination
Scientific validity of answers (problems and theoretical
questions)
Classroom participation
Project presentation and
other homework
30%
10.6 Minimum standards
30% of final examination and 15% of seminar activities
Date of completion
Signature of the Course Instructor
Signature of the Seminar Lecturer
01.10.2018
Date of approval in Department
.........................
.........................
Signature of the Head of the Department
.........................
.........................
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