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Course Description Course Name : Time Series Analysis I Course Code : IEM 709 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. KENAN LOPÇU Learning Outcomes of the Course : Acquires the concepts of ergodicity and stationarity. Understands the Wold decomposition. Solves the univariate stationary processes such as AR, MA and ARMA and perform forecast using these models. Understands the Granger causality and tesst it Applies vector autoregressive (VAR) processes and interprets the impulse response functions and variance decomposition. Applies the above models in a suitable programming language. Interprets the solutions 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 first 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.This semester the focus will be largely on stationary time series processes. We will start with an introduction, including the concepts of erdodicity and stationarity and the Wold decomposition. The univariate stationary processes, Granger causality and vector autoregressive processes will be discussed formally throughout the semester. By the end of the semester students are expected to have a working knowledge of stationary time series models used in econometrics and be able to apply them in a suitable programming language. Course Contents : Introduction and Basic Concepts, Univariate Stationary Processes, Granger Causality, Vector Autoregresive (VAR) Processes. 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 Introduction and Basic Concepts Reading related sources Lecture 2 Introduction and Basic Concepts Reading related sources ,Problem Set and Application Lecture and Problem Session 3 Introduction and Basic Concepts Reading related sources ,Problem Set and Application Lecture, Problem Session and Application 4 Univariate Stationary Processes Reading related sources Lecture 5 Univariate Stationary Processes Reading related sources ,Problem Set and Application Lecture and Problem Session 6 Univariate Stationary Processes 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 Granger Causality Reading related sources Lecture 10 Granger Causality Reading related sources ,Problem Set and Application Lecture and Problem Session 11 Granger Causality Reading related sources ,Problem Set and Application Lecture, Problem Session and Application 12 Vector Autoregressive Processes Reading related sources related sources Lecture 13 Vector Autoregressive Processes Reading related sources ,Problem Set and Application Lecture and Problem Session 14 Vector Autoregressive Processes 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