Information Package / Course Catalogue
| Psychology | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Economics, Administrative and Social Sciences | ||||
| Course Code | ECON 302 | ||||
| Course Title in English | Econometrics II | ||||
| Course Title in Turkish | Ekonometri I | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Seçiniz | ||||
| Semester | Spring | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 136 hours per semester | ||||
| Number of Credits | 5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites |
ECON 301 - Econometrics I |
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| Expected Prior Knowledge | Knowledge of mathematical concepts and statistics | ||||
| Co-requisites | None | ||||
| Registration Restrictions | None | ||||
| Overall Educational Objective | The aim of the course is to provide students the scope and the methodology of econometrics. | ||||
| Course Description | This course is the second part of the course “Econometrics I” and the aim is to provide students the scope and the methodology of econometrics using different techniques. After a brief recap on the first part of the econometrics course, students will first be introduced to regression analysis with panel data. Later, the discussion will be extended to topics such as, regressions with binary explanatory variables, regressions with binary dependent variables, instrumental variable regressions, and time series regression. Each topic will be discussed as a theoretical approach and applications for every topic will be covered during the term. Students will model simple applications using Excel or STATA statistical software on a variety of datasets. The course is quantitatively rigorous and requires knowledge of mathematics and statistics. Upon successful completion of the course, students will be able to conduct simple econometric analysis using different models and gain insight into the interpretation of empirical economic research findings. | ||||
| Course Description in Turkish | Ekonometri dersinin ikinci kısmı olan bu dersın amacı, öğrencilerin ekonometr konusundaki temek bilgilerini kullanarak farklı teknikler öğrenmeleri bu teknikleri basit modellerde uygulayabilip yorumlayabiliyor olmalarını sağlamaktır. Ekonometrı I dersinin kısa bir özetinden sonra, öğrencilere öncelikle panel veri ile regresyon analizi konusu anlatılacaktır. Daha sonra, açıklayıcı değişkenlerle regresyonlar, bağımlı değişkenlerle regresyonlar, ve zaman serisi regresyonu gibi konulara değinilecektir. Her konu teorik olarak ele alınacak ve sonrasında konu uygulamalı örneklerle pekiştirilecektir. Öğrenciler, dönem boyunca çeşitli veri setleri üzerinde Excel veya STATA programını kullanarak basit uygulamalar modelleyeceklerdir. Ders başarı ile tamamlandığında, öğrenci, farklı modeller kullanarak basit ekonometrik analizler yapabiliyor olmalı ve öğrencinin ampirik ekonomik araştırma bulgularının yorumlanmasına ilişkin becerisini geliştirmiş olması beklenmektedir. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Interpret regression results from different techniques 2) Analyze economic data using standard linear regression model 3) Make inference from econometric models and evaluate related results. 4) Model simple applications and interpret the results. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | ||||
| 2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | ||||
| 3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | ||||
| 4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | ||||
| 5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | ||||
| 6) Internalization and dissemination of professional ethical standards. | ||||
| 7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | ||||
| 8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | ||||
| 9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | ||||
| 10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | ||||
| 11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | ||||
| 12) Ability to acquire knowledge independently, and to plan one’s own learning. | ||||
| 13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
| 2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
| 3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
| 4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
| 5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
| 6) | Internalization and dissemination of professional ethical standards. | N | |
| 7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
| 8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
| 9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
| 10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
| 11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
| 12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
| 13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
| Prepared by and Date | NAROD ERKOL , February 2024 |
| Course Coordinator | MUHAMMED ABDULLAH ALTUNDAL |
| Semester | Spring |
| Name of Instructor | Asst. Prof. Dr. NAROD ERKOL |
Course Contents
| Week | Subject |
| 1) | Syllabus, Introduction, ECON 301 Recap |
| 2) | Recap on Linear Regression with Multiple Regressors |
| 3) | Regression with Panel Data |
| 4) | Regressions with Binary Explanatory Variables |
| 5) | Regressions with Binary Explanatory Variables: Application |
| 6) | Regressions with Binary Dependent Variables |
| 7) | Regressions with Binary Dependent Variables: Application |
| 8) | Midterm exam |
| 9) | Instrumental Variable Regression |
| 10) | Instrumental Variable Regression: Application |
| 11) | Times Series Regression and Forecasting |
| 12) | Time Series Regression and Forecasting: Application |
| 13) | Additional Topics in Time Series Regression |
| 14) | Review |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | Stock, J. H., & Watson, M. W. (2020). Introduction to Econometrics (4th Edition). Pearson. Using R for Introductory Econometrics, Florian Heiss. Basic Econometrics, by D. Gujarati, McGraw-Hill, 5th edition. Applied Econometrics with R, by Kleiber and Zeileis, Springer-Verlag, 2008. | ||||||||||||||||||
| Teaching Methods | Active Learning Flipped Learning | ||||||||||||||||||
| Homework and Projects | Pre and post class assignments | ||||||||||||||||||
| Laboratory Work | NA | ||||||||||||||||||
| Computer Use | Yes | ||||||||||||||||||
| Other Activities | NA | ||||||||||||||||||
| Assessment Methods |
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| Course Administration |
erkoln@mef.edu.tr 02123953670 Course Instructor: Asst. Prof. Narod Erkol (erkoln@mef.edu.tr) Attendance/participation: Students are expected to prepare for the lecture via pre-class assignments, videos and reading materials. Students are responsible to follow the announcements, course materials available on Blackboard system. Formal use of e-mails: Students are expected to use their @mef accounts for email traffic. The instructor is only responsible for the information sent/received through Blackboard system and emails using @mef account. The course instructor assumes that any information sent through email will be received in 24 hours, unless a system problem occurs. Grading and evaluation: Evaluation will be based on the student learning outcomes. It is strongly recommended to complete all the work in a timely fashion. Late submissions will not be accepted. Missing projects: No make up unless a legitimate proof of absence is presented. Missing final exam: Faculty regulations. Academic integrity: All students of MEF University are expected to be honest and comply with academic integrity. Students are expected to do their own work and neither give nor receive unauthorized assistance. Disciplinary action will be taken in case of suspicion. Improper behavior, academic dishonesty and plagiarism: Law on Higher Education Article 54. Important: If the learner cannot collect at least 30 points from the activities other than the final exam, they can not take the final exam and will get an FZ grade. |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Calculation | |||
| No/Weeks per Semester | |||||
| Course Hours | 28 | 140 | |||
| Homework Assignments | 8 | 64 | |||
| Midterm(s) | 2 | 34 | |||
| Final Examination | 2 | 34 | |||
| Total Workload | 272 | ||||
| Total Workload/25 | 10.9 | ||||
| ECTS | 5 | ||||