Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
1ECON 601Advanced Research Methods3+0+038

Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program PhD Program in Economics (English)
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course This course covers empirical analysis techniques used to investigate the relationships between financial and economic variables.
Course Content This course covers empirical analysis techniques used to investigate the relationships between financial and economic variables. Under this context, topics such as linear regression, multiple regression, dummy variables, heteroscedasticity, hypothesis testing, omitted variables and misspecification, asymptotic theory, measurement error, instrumental variables, Non-Linear least squares estimation, maximum likelihood estimation, generalized least squares estimation, will be explained through empirical examples. The model selection will be taught in greater detail. Software programs, namely MS Excel, E-views and Stata, will be used during the course work.
Course Methods and Techniques Lectures and Practice Sessions
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Asad ul Islam Khan
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources W. Greene, Econometric Analysis, 7th ed., Pearson Education Limited 2012
R. Davidson and J.G. MacKinnon, Econometric Theory and Methods, Oxford University Press, 2003
Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 6th ed., South Western, 2013
Johnston ve J. DiNardo, Econometric Methods, McGraw-Hill
Zaman A., Statistical Foundations for Econometrics Techniques, 1996
• [EA]: W. Greene, Econometric Analysis, 7th ed., Pearson Education Limited 2012.
• [ETM]: R. Davidson and J.G. MacKinnon, Econometric Theory and Methods, Oxford University Press, 2003.
• [CN]: Class Notes and Handouts
• [IE]: Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 6th ed., South Western, 2013.
• [EM]: Johnston ve J. DiNardo, Econometric Methods, McGraw-Hill
• [SF]: Zaman A., Statistical Foundations for Econometrics Techniques, 1996
TBA
TBA
TBA

Course Category
Mathematics and Basic Sciences %50
Social Sciences %20
Field %30

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Final examination 1 % 40
Total
2
% 70

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Assignments 1 27 27
Mid-terms 1 3 3
Laboratory 14 3 42
Project 1 81 81
Final examination 1 3 3
Total Work Load   Number of ECTS Credits 8 240

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 To estimate simple linear and multiple regression using OLS, IVE, GIVE, GMM, MLE, GLS, WLS, HSCSE
2 To evaluate hypothesis testing
3 To improve competency in selecting the most appropriate modeling approach
4 To interpret basic econometric models based on economic theory.
5 To write a complete research paper using econometrics/quantitative methods clearly stating the research gap in already existing literature.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Conceptual Foundations & How to carry out a research project-I • to read the relevant chapters before the lecture hours IE Ch 19, CN
2 Conceptual Foundations & How to carry out a research project-II • to read the relevant chapters before the lecture hours IE Ch 19, CN
3 Simple Regression Model, Principle of Least Squares (OLS) • to read the relevant chapters before the lecture hours EA Ch 1-3, ETM Ch 1-2, EM Ch 1 & 3, CN
4 Multiple Linear Regression Model Properties of OLS estimator • to read the relevant chapters before the lecture hours EA Ch 3-4, ETM Ch 2-3, EM Ch 1 & 3, CN
5 Inferences in Classical Model • to read the relevant chapters before the lecture hours EA Ch 4,5 ETM Ch 3,4 EM Ch 3, CN
6 Further Issues in Classical Model (Diagnostic Tests) • to read the relevant chapters before the lecture hours EA Ch 5-6, ETM Ch 5 & 15, EM Ch 4 & 6, CN
7 Model Selection-I • to read the relevant chapters before the lecture hours EA Ch 5-6, ETM Ch 5 & 15, EM Ch 4 & 6, CN
8 Model Selection-II • to read the relevant chapters before the lecture hours EA Ch 5-6, ETM Ch 5 & 15, EM Ch 4 & 6, CN
9 Multiple Regression Analysis: Qualitative Information (Dummy Variable) • to read the relevant chapters before the lecture hours IE Ch 7, CN
10 Non Linear Regression (NLS), Generalized Least Squares (GLS) • to read the relevant chapters before the lecture hours EA Ch 7, 9 ETM Ch 6-7 EM Ch 5, CN
11 Maximum Likelihood Estimation (MLE) • to read the relevant chapters before the lecture hours EA Ch 9 ETM Ch 7 EM Ch 5, CN
12 Estimation in presence of Endogeneity: GIVE • to read the relevant chapters before the lecture hours EA Ch 8, ETM Ch 8, EM Ch 5
13 Simultaneous Equations Model Generalized Method of Moments • to read the relevant chapters before the lecture hours EA Ch 13, ETM Ch 9, EM Ch 10, CN
14 Binary/ Discrete Dependent Variable • to read the relevant chapters before the lecture hours ETM Ch 11, EM Ch 13, CN


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
All 5 5 5 5 4
C1 5 5 5 5 4
C2 5 5 5 4 5
C3 5 5 5 5 4
C4 5 5 5 5 4
C5 5 5 5 5 5

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


https://obs.ihu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=249274&lang=en