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Course Description Course Name : Econometrics II Course Code : IEM 702 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Prof.Dr. HASAN ALTAN ÇABUK Learning Outcomes of the Course : Improves his/her level of knowledge in econometrics Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : The aim of this course is to teach advanced methods of econometrics to students who received BA Degree in Econometrics, Mathematics and Statistics. Course Contents : Multiple linear regression model, least squares estimation, finite sample properties of OLS estimators, asymptotic properties. Functional and structural changes in the format, binary variables, and modeling of structural break test model stability test. Non-linear regression models, generalized regression model, the problem of heteroscedasticity, multiple correlation, panel data models, regression equations of my site, simultaneous equations model, maximum likelihood estimation, models with lagged variables, time series 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 Repeating Multiple Linear Regression Model and Linear Hypothesis Testing Reading relavant parts in source books according to weekly program Lecture, problem solving 2 Deviations from the classical linear regression model: Multicollinearity Reading relavant parts in source books according to weekly program Lecture, problem solving 3 Deviations from the classical linear regression model: autocorrelation Reading relavant parts in source books according to weekly program Lecture, problem solving 4 Deviations from the classical linear regression model: heterocedasticity Reading relavant parts in source books according to weekly program Lecture, problem solving 5 Deviations from the classical linear regression model: Revision Reading relavant parts in source books according to weekly program Lecture, problem solving 6 Structural change and chow test Reading relavant parts in source books according to weekly program Lecture, problem solving 7 Paper work Reading relavant parts in source books according to weekly program Lecture, problem solving 8 Midterm examination 9 Structural change and Dummy variables Reading relavant parts in source books according to weekly program Lecture, problem solving 10 Multi-Equation Econometric Models: Structural Model and Reduced-Form Reading relavant parts in source books according to weekly program Lecture, problem solving 11 Multiple Equation Econometric Models: Investigation of identifiability of structural model Reading relavant parts in source books according to weekly program Lecture, problem solving 12 Multiple Equation Econometric Models:Investigation of identifiability of reduced-form Reading relavant parts in source books according to weekly program Lecture, problem solving 13 Multiple Equation Econometric Models: Investigation of identifiability of reduced-form Reading relavant parts in source books according to weekly program Lecture, problem solving 14 Paper wok Reading relavant parts in source books according to weekly program Lecture, problem solving 15 Writing article Reading relavant parts in source books according to weekly program Lecture, problem solving 16/17 Final examination   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Johnston, J. Econometric Methods, McGraw Hill Book, 1986 Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 60     Homeworks/Projects/Others 5 40 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 4 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 3 6 Uses advanced software packages concerning Econometrics, Statistics, and Operation Research 2 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 3 8 Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. 2 9 Questions traditional approaches and their implementation and develops alternative study programs when required 3 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. 5 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. 5 16 Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently 5 * 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 3 42 Assesment Related Works     Homeworks, Projects, Others 5 10 50     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 10 10 Total Workload: 154 Total Workload / 25 (h): 6.16 ECTS Credit: 6