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Course Description Course Name : Time Series Analysis II Course Code : IEM 710 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. KENAN LOPÇU Learning Outcomes of the Course : Acquires the forms of nonstationarity. Applies the unit root tests. Understands the concepts of spurious regression and cointegration. Applies the single equation static and dynamic cointegration tests. Acquires and applies the cointegration tests in vector autoregressive (VAR) models. Acquires and applies the autoregressive conditional heteroskedasticity (ARCH) and generalized conditional heteroskedasticity (GARCH) models. Interprets the results obtained. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : This course is the second part of a two semester sequence designed to provide students with basic concepts and methods in time series econometrics. Students enrolled in this class are expected to have a basic knowledge of calculus and matrix algebra as well as of econometrics and statistics at the level of introductory textbooks and to have successfully completed IEM 709 Time Series Analysis I. This semester the focus will be largely on nonstationary time series processes. We will start with the forms and tests of nonstationary processes, and Coıntegration and autoregressive conditional heteroskedasticity (ARCH) and generalized conditional heteroskedasticity (GARCH) will be discussed formally throughout the semester. By the end of the semester students are expected to have a working knowledge of time series models used in econometrics and be able to apply them in a suitable programming language. Course Contents : Nonstationary Processes, Cointegration, Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Conditional Heteroskedasticity (GARCH) Models. Language of Instruction : Turkish Work Place : Classroom   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Forms of Nonstationarity Reading related sources Lecture 2 Trend Elimination Reading related sources, Problem Set and Application Lecture and Problem Session 3 Unit Root Tests Reading related sources, Problem Set and Application Lecture, Problem Session and Application 4 Cointegrated Processes: Definition and Properties Reading related sources, Problem Set and Application Lecture 5 Cointegration in Single Equation Models Reading related sources, Problem Set and Application Lecture and Problem Session 6 Cointegration in Single Equation Models Reading related sources, Problem Set and Application Lecture, Problem Session and Application 7 Review and Thoughts on the Midterm Exam Reading related sources, Problem Set and Application Lecture, Problem Session and Application 8 Midterm Exam 9 Cointegration in VAR Models Reading related sources, Problem Set and Application Lecture 10 Cointegration in VAR Models Reading related sources, Problem Set and Application Lecture and Problem Session 11 Cointegration in VAR Models Reading related sources, Problem Set and Application Lecture, Problem Session and Application 12 ARCH Models: Definition and Representation Reading related sources, Problem Set and Application Lecture 13 GARCH Models Reading related sources, Problem Set and Application Lecture and Problem Session 14 ARCH/GARCH Models: Estimation and Testing Reading related sources, Problem Set and Application Lecture, Problem Session and Application 15 Review and Thoughts on the Final Exam Reading related sources, Problem Set and Application Lecture, Problem Session and Application 16/17 Final Exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Gebhard Kirchgässner and JürgenWolters, Introduction to Modern Time Series Analysis, Springer-Verlag, 2007  Lecture Notes Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 40     Homeworks/Projects/Others 6 60 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Explains Econometric concepts 5 2 Equipped with the foundations of Economics, develops Economic models 5 3 Models problems using the knowledge of Mathematics, Statistics, and Econometrics 5 4 Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems 5 5 Collects, edits, and analyzes data 5 6 Uses advanced software packages concerning Econometrics, Statistics, and Operation Research 5 7 Develops the ability to use different resources in an area which has not been studied in the scope of academic rules, synthesizes the information gathered, and gives effective presentations 5 8 Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. 3 9 Questions traditional approaches and their implementation and develops alternative study programs when required 4 10 Recognizes and implements social, scientific, and professional ethic values 3 11 Gives a consistent estimate for the model and analyzes and interprets its results 5 12 Takes responsibility individually and/or as a member of a team; leads a team and works effectively 5 13 Defines the concepts of statistics, operations research and mathematics. 4 14 Knowing the necessity of life-long learning, follows the latest developments in the field of study and improves himself continiously 5 15 Follows the current issues, and interprets the data about economic and social events. 4 16 Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently 3 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 4 56 Assesment Related Works     Homeworks, Projects, Others 6 7 42     Mid-term Exams (Written, Oral, etc.) 1 5 5     Final Exam 1 5 5 Total Workload: 150 Total Workload / 25 (h): 6 ECTS Credit: 6