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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 ......................... .........................