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Course Description Course Name : Statistical Estimation Course Code : IEM 763 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. HÜSEYİN GÜLER Learning Outcomes of the Course : Defines the appropriate estimator for a problem Knows the small sample properties of estimators Knows the asymptotic properties of estimators Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : Estimation methods used in statistics and econometrics will be considered. The course aims to provide the students with the ability to build the fundamentals of estimation theory that they are going to use in their research in MS and PhD studies. Course Contents : In this course, estimation methods, estimators, and the small and large sample properties of estimators will be examined. Language of Instruction : Turkish Work Place : Department of Econometrics, meeting room   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Point estimates: Estimators; Distributions of estimators; Bias, variance and mean square error Reading the related chapters in the reference books Lecture 2 Point estimates: Estimators; Distributions of estimators; Bias, variance and mean square error Reading the related chapters in the reference books Lecture, discussion, simulation 3 Small sample properties of estimators: Unbiasedness; Sufficiency; Efficiency Reading the related chapters in the reference books Lecture, simulation 4 Minimum variance unbiased estimator Reading the related chapters in the reference books Lecture, discussion 5 Maximum likelihood estimator Reading the related chapters in the reference books Lecture, discussion 6 Method of moments Reading the related chapters in the reference books Lecture, discussion 7 Rao-Blackwell and Cramer-Rao theorems Reading the related chapters in the reference books Lecture 8 Midterm exam 9 Large sample properties of estimators: Consistency; Asymptotic unbiasedness; Asymptotic normality; Asympotic efficiency Reading the related chapters in the reference books Lecture, discussion, simulation 10 Large sample properties of estimators: Consistency; Asymptotic unbiasedness; Asymptotic normality; Asympotic efficiency Reading the related chapters in the reference books Lecture, discussion, simulation 11 Asymptotic properties of maximum likelihood estimator Reading the related chapters in the reference books Lecture, discussion, simulation 12 Interval estimate Reading the related chapters in the reference books Lecture, discussion, simulation 13 Interval estimate Reading the related chapters in the reference books Lecture, discussion, simulation 14 Hypothesis tests: Simple and composite hypothesis; Likelihood ratio test Reading the related chapters in the reference books Lecture, discussion, simulation 15 Hypothesis tests: Simple and composite hypothesis; Likelihood ratio test Reading the related chapters in the reference books Lecture, discussion, simulation 16/17 Final exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Statistical Inference, George Casella, Roger L. Berger, 2001, Duxbury.  Introduction to Mathematical Statistics, 7/E, Robert V. Hogg, Joeseph McKean, Allen T Craig, Pearson, 2013. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 50     Homeworks/Projects/Others 4 50 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 3 2 Equipped with the foundations of Economics, develops Economic models 0 3 Models problems using the knowledge of Mathematics, Statistics, and Econometrics 4 4 Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems 3 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. 1 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 1 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 3 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 1 15 Follows the current issues, and interprets the data about economic and social events. 0 16 Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently 1 * 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 5 70 Assesment Related Works     Homeworks, Projects, Others 4 5 20     Mid-term Exams (Written, Oral, etc.) 1 12 12     Final Exam 1 15 15 Total Workload: 159 Total Workload / 25 (h): 6.36 ECTS Credit: 6