| School/Faculty/Institute |
Faculty of Econ., Admin. and Social Sciences |
| Course Code |
ECON 301 |
| Course Title in English |
Econometrics I |
| Course Title in Turkish |
Ekonometri I |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Seçiniz |
| Semester |
Fall |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 0 |
Other: 0 |
|
| Estimated Student Workload |
136 hours per semester |
| Number of Credits |
5 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
MATH 204 - Probability and Statistics for Social Sciences II
|
| 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 is first part of econometrics course and the aim is to provide students the scope and the methodology of econometrics. After a brief refresher on probability and statistics, students will first be introduced to univariate regression analysis (its theory, the statistical and economic interpretation of regression results, etc.). Later, the discussion will be extended to topics such as the problem of estimation in regression models, multivariate regression analysis and nonlinear regression functions. Each topic will be discussed as a theoretical approach and applications for every topic will be covered during the term. 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 linear models and gain insight into the interpretation of empirical economic research findings. |
| Course Description in Turkish |
Ekonometri dersinin ilk kısmı olan bu dersın amacı, öğrencilerin ekonometrinin ana konu başlıklarına tanıdık hale gelmelerini sağlamaktır. Olasılık ve istatistik konuları üzerinde birer haftalık bir hatırlatma yaptıktan sonra, öğrencilere öncelikle tek değişkenli regresyon analizini (teorisi, regresyon sonuçlarının istatistiksel ve ekonomik yorumu vb.) tanıtılacakdır. Daha sonra, regresyon modellerinde tahmin problemi, çok değişkenli regresyon analizi ve doğrusal olmayan regresyon fonksiyonları gibi konulara değinilecektir. Her konu teorik olarak ele alınacak ve sonrasında konu uygulamalı örneklerle pekiştirilecektir. Ders başarı ile tamamlandığında, öğrenci, doğrusal modeller kullanarak basit ekonometrik analizler yapabiliyor olmalı ve öğrencinin ampirik ekonomik araştırma bulgularının yorumlanmasına ilişkin becerisini geliştirmiş olması beklenmektedir. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Understand the techniques of the univariate and multivariate regression analysis and interpret the results.
2) Analyze economic data using standard linear regression model
3) Make inference from econometric models and evaluate related results.
|
| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
| 1) Has a broad understanding of economics with a deep exposure to other social sciences and mathematics. |
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| 2) Demonstrates knowledge and skills in understanding the interactions of different areas of economics. |
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| 3) Displays a sound comprehension of microeconomic and macroeconomic theory.
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| 4) Applies economic concepts to solve complex problems and enhance decision-making capability. |
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| 5) Uses quantitative techniques to analyze different economic systems.
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| 6) Applies theoretical knowledge to analyze issues regarding Turkish and global economies. |
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| 7) Demonstrates proficiency in statistical tools and mainstream software programs to process and evaluate economic data. |
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| 8) Behaves according to scientific and ethical values at all stages of economic analysis: data collection, interpretation and dissemination of findings.
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| 9) Uses written and spoken English effectively (at least CEFR B2 level) to exchange scientific information. |
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| 10) Exhibits individual and professional ethical behavior and social responsibility. |
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| 11) Displays learning skills necessary for further study with a high degree of autonomy |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
| |
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| |
Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
Has a broad understanding of economics with a deep exposure to other social sciences and mathematics. |
H |
|
| 2) |
Demonstrates knowledge and skills in understanding the interactions of different areas of economics. |
S |
|
| 3) |
Displays a sound comprehension of microeconomic and macroeconomic theory.
|
S |
|
| 4) |
Applies economic concepts to solve complex problems and enhance decision-making capability. |
S |
|
| 5) |
Uses quantitative techniques to analyze different economic systems.
|
H |
|
| 6) |
Applies theoretical knowledge to analyze issues regarding Turkish and global economies. |
N |
|
| 7) |
Demonstrates proficiency in statistical tools and mainstream software programs to process and evaluate economic data. |
H |
|
| 8) |
Behaves according to scientific and ethical values at all stages of economic analysis: data collection, interpretation and dissemination of findings.
|
H |
|
| 9) |
Uses written and spoken English effectively (at least CEFR B2 level) to exchange scientific information. |
S |
|
| 10) |
Exhibits individual and professional ethical behavior and social responsibility. |
S |
|
| 11) |
Displays learning skills necessary for further study with a high degree of autonomy |
S |
|
| Prepared by and Date |
NAROD ERKOL , February 2024 |
| Course Coordinator |
NAROD ERKOL |
| Semester |
Fall |
| Name of Instructor |
Asst. Prof. Dr. NAROD ERKOL |
Course Contents
| Week |
Subject |
| 1) |
Syllabus and Introduction to econometrics,
Economic Questions and Data
|
| 2) |
A Review of Probability: Characteristics of Probability Distributions |
| 3) |
A review of some statistical concepts including
statistical Inference: Estimation and Hypothesis Testing
|
| 4) |
Nature of Regression Analysis |
| 5) |
Linear Regression with One Regressor |
| 6) |
Two-Variable Regression Analysis: Some Basic Ideas |
| 7) |
Regression with a Single Regressor: Hypothesis Tests |
| 8) |
Midterm Exam |
| 9) |
Regression with a Single Regressor: Confidence Intervals |
| 10) |
Two-Variable Regression Model: The Problem of Estimation |
| 11) |
Linear Regression with Multiple Regressors |
| 12) |
Multiple Regression Analysis: The Problem of Estimation |
| 13) |
Hypothesis Tests and Confidence Intervals in Multiple Regression |
| 13) |
Nonlinear Regression Functions |
| 15) |
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 |
| Assessment Tools |
Count |
Weight |
| Attendance |
14 |
% 0 |
| Quiz(zes) |
3 |
% 5 |
| Homework Assignments |
2 |
% 30 |
| Midterm(s) |
1 |
% 30 |
| Final Examination |
1 |
% 35 |
| TOTAL |
% 100 |
|
| 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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Information Package / Course Catalogue