Applied Econometrics (ECON521) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Applied Econometrics ECON521 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language Turkish
Course Type N/A
Course Level Ph.D.
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Discussion, Question and Answer, Drill and Practice, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Nil Demet Güngör
Course Assistants
Course Objectives The aim of this course is to introduce students to the study of econometrics, which deals with the application of statistical methods to test economic theory. Econometrics uses observational data to estimate economic relationships, test hypotheses about economic behaviour, and predict future values of economic variables. Software applications are introduced during the course in order to provide hands-on experience with data collection, analysis and interpretation.
Course Learning Outcomes The students who succeeded in this course;
  • distinguish between different types of data used in econometric analysis
  • understand the use of econometric methods in estimating causal relationships and building models in economics and related fields
  • estimate and interpret the results of empirical models
  • use econometric software in simple applications
Course Content Modeling linear regressions, bivariate and multivariate regression techniques and their applications, model specification problems, parameter estimation problems, nonlinear regression models, data handling problems, simultenaous equation models, restricted regression models, time series, nonstationary series and autocorrelation and panel data.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Review of Basic Statistics - Descriptive Statistics, Probability and Random variables; Introduction – The Methodology of Economics Gujarati, Introduction: pp. 1-13
2 The Nature of Regression Analysis – Causation, Correlation and Types of Data Gujarati, Chapter 1: pp. 15-32
3 Two Variable Regression Model: Some Basic Ideas Gujarati, Chapter 2: pp. 37-52
4 Two Variable Regression Model: The Problem of Estimation Gujarati, Chapter 3: pp. 58-105
5 Two Variable Regression Model: The Problem of Estimation; Gujarati, Chapter 3: pp. 58-105
6 The Normality Assumption: Classical Normal Linear Regression Model Gujarati, Chapter 4: pp. 107-113
7 Two-Variable Regression: Interval Estimation and Hypothesis Testing Gujarati, Chapter 5: pp. 119-133
8 Two-Variable Regression Model: Interval Estimation and Hypothesis Testing Gujarati, Chapter 5: pp. 134-150
9 MIDTERM EXAM
10 Introduction to Eviews Class Handouts
11 Extensions of the Two-Variable Regression Model: Scaling, Functional Forms Gujarati, Chapter 6: pp. 164-193
12 Multiple Regression Model: The Problem of Estimation Gujarati, Chapter 7: pp. 202-232
13 Multiple Regression Model: The Problem of Inference Gujarati, Chapter 8: pp. 248-263
14 Multiple Regression Model: The Problem of Inference Gujarati, Chapter 8: pp. 264-280
15 General Review
16 Final Exam

Sources

Course Book 1. Gujarati, Damodar N. (2003) Basic Econometrics, 4th Edition, New York and Boston: McGraw-Hill.
2. Gujarati, Damodar N. (2003) Temel Ekonometri, Literatür Kitabevi, McGraw-Hill.
Other Sources 3. Wooldridge, Jeffrey (2008) Introductory Econometrics: A Modern Approach (with Economic Applications), 4th Edition, Cengage Learning.
4. Peter J. Kennedy (1998) A Guide to Econometrics, 4th Edition, MIT Press.
5. Ramanathan, R. (2002), Introductory Econometrics with Applications, 5th edition, Orlando, FL: Harcourt College Publishers.
6. Hill, R.C., Griffiths, W.E. and G. G. Judge (2001) Undergraduate Econometrics, 2nd Edition, John Wiley and Sons, Inc.
7. Hill, R.C., Griffiths, W.E. and G. G. Judge (2000) Using Eviews For Undergraduate Econometrics, 2nd Edition, Wiley.
8. Asteriou, D. (2006) Applied Econometrics: A Modern Approach using EViews and Microfit, Palgrave-Macmillan.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 5 25
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 45
Toplam 7 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 To compare main theories and/or approaches in political economy and make a critical evaluation of each
2 To compare main macroeconomic theories and/or approaches and make a critical evaluation of each
3 To use complementary approaches from other relevant disciplines (e.g. political science, sociology) in order to solve problems requiring scientific expertise
4 To develop the skills for establishing a micro-macro link in human and social sciences
5 To analyze the main economic indicators and comment on them
6 To acquire theoretical knowledge through literature survey and derive empirically testable hypothesis
7 To be able to develop new approaches/theories for complex problems in political economy
8 To apply critical thinking, statistical/econometric tools or other relevant quantitative and qualitative tools to new areas/problems
9 To make a research design and carry it out within predetermined time frames
10 To formulate and present policy recommendations based on academic research
11 To continue learning and undertake advanced research independently

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 4 64
Presentation/Seminar Prepration
Project
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 2 2
Prepration of Final Exams/Final Jury 1 3 3
Total Workload 117