Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
5ECON 301Econometrics I3+2+04506.11.2023
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program BA Program in Economics
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course The emphasis of this course will be on understanding the tools of econometrics and applying them in practice.
Course Content Econometrics is a set of research tools used to estimate and test economic relationships. It is a social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena. This course is an undergraduate level introduction to econometrics and the methods taught in this introductory course can also be employed in the disciplines of accounting, finance, marketing and management and in many other social science disciplines. The emphasis of this course will be on understanding the tools of econometrics and applying them in practice. You will study and apply regression analysis to various data sets in order to familiarize you with the core concepts of estimation of economic parameters, prediction of economic outcomes, and statistical inference.
Course Methods and Techniques
Prerequisites and co-requisities ( STAT 102 )
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Asad ul Islam Khan
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources EA: W. Greene, “Econometric Analysis”, 7th ed., Pearson Education Limited 2012.
BE: Gujarati, Damodar N “Basic Econometrics”. Tata McGraw-Hill Education, 2009.
IEF: Brooks, C. “Introductory Econometrics for Finance”, 3rd ed., 2014.
Introductory Econometrics: A Modern Approach, 6th edition Jeffrey M. Wooldridge
Brooks, C. Introductory Econometrics for Finance, 3rd ed., 2014.
Course Notes IEMA: “Introductory Econometrics: A Modern Approach”, 6th edition Jeffrey M. Wooldridge
IEF: Brooks, C. “Introductory Econometrics for Finance”, 3rd ed., 2014.
EA: W. Greene, “Econometric Analysis”, 7th ed., Pearson Education Limited 2012.
BE: Gujarati, Damodar N “Basic Econometrics”. Tata McGraw-Hill Education, 2009.
Documents TBA
Assignments TBA
Exams 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
Assignment 2 % 30
Final examination 1 % 40
Total
4
% 100

 
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 2 16 32
Mid-terms 1 3 3
Laboratory 14 2 28
Final examination 1 3 3
Total Work Load   Number of ECTS Credits 5 150

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Practice and exercise the basic econometric methodology
2 Use a statistical/econometric computer package to estimate an econometric model
3 Report the results of your work in a non-technical and literate manner.
4 Estimate and interpret linear regression models
5 Criticize, evaluate and assess reported regression results in applied academic papers
6 Interpret the results for someone who is not trained as an economist.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Review of Statistics and Probability, The Nature of Econometrics and Economic Data to read the relevant chapters and papers before the lecture hours Appendix A, B & C Chapter 1
2 Simple Linear Regression to read the relevant chapters and papers before the lecture hours Chapter 2
3 Multiple Regression Analysis: Estimation • to read the relevant chapters and papers before the lecture hours Chapter 3
4 Multiple Regression Analysis: Inference • to read the relevant chapters and papers before the lecture hours Chapter 4
5 Multiple Regression Analysis: Properties of OLS estimators • to read the relevant chapters and papers before the lecture hours Chapter 5
6 Multiple Regression Analysis: Further Issues • to read the relevant chapters and papers before the lecture hours Chapter 6
7 Multiple Regression Analysis with Qualitative Information • to read the relevant chapters and papers before the lecture hours Chapter 7
8 More on Specification and Data Issues • to read the relevant chapters and papers before the lecture hours Chapter 9
9 Model Selection • to read the relevant chapters and papers before the lecture hours Chapter 9
10 Multicollinearity, Heteroscedasticity • to read the relevant chapters and papers before the lecture hours Chapter 8
11 Model’s Validity Testing/Residuals Diagnosis Tests • to read the relevant chapters and papers before the lecture hours Chapter 8, 9
12 Instrumental Variables Estimation and Two Stage Least Squares • to read the relevant chapters and papers before the lecture hours Chapter 15
13 Simultaneous Equations Models-I • to read the relevant chapters and papers before the lecture hours Chapter 16
14 Simultaneous Equations Models-II • to read the relevant chapters and papers before the lecture hours Chapter 16

 
Recommended Optional Programme Components
Veri yok
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 2 3 4 4 5
C1 1 5 5 2 4
C2 1 3 5 2 5
C3 1 2 3 5 5
C4 1 5 5 5 3
C5 5 2 2 5 4
C6 1 2 2 5 5

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