Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
6ECON 302Econometrics II3+2+04513.02.2024
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 It provides students with an understanding of the empirical techniques commonly used in economic research; the ability to use these empirical techniques; the ability to critically evaluate and interpret empirical work in economics; expertise in the use of an appropriate software package.
Course Content Econometrics is a branch of economics. It applies mathematical and statistical methods to explore and quantify the relationships between economic, financial and social variables where these relationships are either hypothesized by models or based on observed phenomena. This course covers different estimation methodologies used in Econometrics and detailed discussion on Time Series Analysis. Time series models ranging from univariate models, single equation multivariate models and multiple equation models are discussed in detail.
Course Methods and Techniques
Prerequisites and co-requisities ( ECON 301 )
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Asad Ul Islam KHAN
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources • [TSA]: Time Series Analysis with Applications in R, Springer (in IHU library) • [CN]: Class Notes and Handouts
• [ETM]: Econometric Theory and Methods, Oxford University Press, 2003. (in IHU library)
• [ATE]: Applied Time Series Econometrics, Cambridge University Press. (in IHU library) • [GME]: Marno Verbeek, A Guide to Modern Econometrics, 2nd Ed. John Wiley & Sons Ltd. (in IHU library) • [EM]: Johnston ve J. DiNardo, Econometric Methods, McGraw-Hill
Course Notes [ATE]: Applied Time Series Econometrics, Cambridge University Press. (in IHU library) [GME]: Marno Verbeek, A Guide to Modern Econometrics, 2nd Ed. John Wiley & Sons Ltd. (in IHU library)
[EM]: Johnston ve J. DiNardo, Econometric Methods, McGraw-Hill
[ETM]: Econometric Theory and Methods, Oxford University Press, 2003. (in IHU library)
[TSA]: Time Series Analysis with Applications in R, Springer (in IHU library)
[CN]: Class Notes and Handouts

Course Category
Social Sciences %100

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 % 50
Final examination 1 % 50
Total
2
% 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 5 70
Mid-terms 1 3 3
Practice 14 2 28
Final examination 1 3 3
Total Work Load   Number of ECTS Credits 5 146

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Describe and explain different estimation methodologies, used in Econometrics and their merits and demerits.
2 Describe and explain a variety of frequently used time series models theoretically and empirically by applying them to real data.
3 Have deep insights to the underlying assumptions and theories by simulation of simple time series processes.
4 Have profound expertise in using different application packages like EVIEWS, STATA to model the economic time series.
5 Carry out an independent time series analysis, starting from appropriate model selection, then estimation, validation, interpretation of the estimates and ending it with the provision of policy recommendations.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to the Course, Some introductions and revisions about Statistics and Econometrics • to read the relevant chapters before the lecture hours CN
2 OLS, WLS, GLS, IVE, GIVE and GMM • to read the relevant chapters before the lecture hours ETM Ch 7, EM Ch 5, ETM, CN
3 Maximum Likelihood Estimation (MLE) • to read the relevant chapters before the lecture hours, GME Ch6, ETM Ch 6, CN
4 Binary Response Models • to read the relevant chapters before the lecture hours ETM Ch 11, EM Ch 13, CN
5 Introduction to Time Series Univariate Time Series: AR, MA and ARMA processes • to read the relevant chapters before the lecture hours ATE Ch2, EM Ch7
6 Unit root /Stationarity Time Series • to read the relevant chapters before the lecture hours ATE Ch2, EM Ch7
7 Models for Stationary Time Series • to read the relevant chapters before the lecture hours TSA Ch1,2,3,4, CN
8 Models for Non-Stationary Time Series, Model Specification and Parameter Estimation • to read the relevant chapters before the lecture hours TSA Ch5,6,7, EM Ch7, CN
9 Model Diagnostics, Forecasting & Seasonal Models • to read the relevant chapters before the lecture hours TSA Ch8,9,10, CN
10 Multivariate Single Equation Time Series Models (DL, ARDL Models) • to read the relevant chapters before the lecture hours GME Ch9, TSA Ch 11, EM Ch8, CN
11 Cointegration, Error Correction Model • to read the relevant chapters before the lecture hours GME Ch9, EM Ch8, CN
12 Vector Auto Regressive Models, Cointegration in VAR • to read the relevant chapters before the lecture hours GME Ch9, ATE Ch3, EM Ch8, CN
13 Granger Causality, Vector Error Correction Model • to read the relevant chapters before the lecture hours ATE Ch3, GME Ch 9, EM Ch8, CN
14 Models for Financial Time Series • to read the relevant chapters before the lecture hours TSA Ch12, GME Ch 8, ATE Ch5, CN

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

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