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Course Description Course Name : Econometric models Course Code : İSB462 Course Type : Optional Level of Course : First Cycle Year of Study : 4 Course Semester : Spring (16 Weeks) ECTS : 5 Name of Lecturer(s) : Assoc.Prof.Dr. MAHMUDE REVAN ÖZKALE Learning Outcomes of the Course : Describe econometrics and econometric model Check the validity of the assumptions Use appropriate methods in case of deviation from the model assumptions Distinguish appropriate estimation methods of models Select the correct model that fits the data for statistical analysis Comment on the results obtained using the statistical package programs Evaluate the results of analysis Explain the difference between the models Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : Statistical modeling and interpeting the econometric data Course Contents : Multiple linear regression model, heteroscedasticity, multicollineairt problem, dummy variable models, distributed lag models Language of Instruction : Turkish Work Place : Faculty of Arts and Sciences Annex Classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Introduction to Econometrics, examination of the deviations from the assumptions of multiple regression analysis Source reading Lecture 2 Investigate the properties of the estimators, hypothesis testing in multiple lnear regession model Source reading Lecture, problem-solving 3 Confidence interval in multiple lnear regession model, matrix approximaitons to multiple linear regression model Source reading Lecture, problem-solving 4 Multicollinearity problem (identification and correction of multicollinearity) Source reading Lecture, problem-solving 5 Some biased estimators in the problem of multicollinearity Source reading Lecture, problem-solving 6 Determination of heteroscedasticity, systematic and non-systematic tests (Goldfeld Quant, Park ve Glejser testsi) Source reading Lecture, problem-solving 7 Breusch Pagan Godfrey test from systematic test and correction of heteroscedasticity Source reading Lecture, problem-solving 8 Midterm exam Review the topics discussed in the lecture notes and sources Written exam 9 Dummy variable models Source reading Lecture, problem-solving 10 Dummy variable models Source reading Lecture, problem-solving 11 Qualitative dependent variable regression models (DOM and Logit models) Source reading Lecture, problem-solving 12 Qualitative dependent variable regression models (Logit and Probit models) Source reading Lecture, problem-solving 13 Distributed Lag models (estimation by least squares, Koyck model and Almon polynomial lag model) Source reading Lecture, problem-solving 14 Distributed Lag models (estimation by Nerlove´s partial adjustment model and Cagan´s adptive expectation model) Source reading Lecture, problem-solving 15 Autoregressive models Source reading Lecture, problem-solving 16/17 Final exam Review the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  1. Gujarati, D. N. (çev. Şenesen, Ü., Şenesen, G. G.) (1999), Temel Ekonometri. Literatür Yayıncılık 2. Koutsoyiannis, A. (çev. Şenesen, Ü., Şenesen, G. G.) (1989), Ekonometri Kuramı. Verso Yayıncılık Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 100     Homeworks/Projects/Others 0 0 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 Utilize computer systems and softwares 0 2 Apply the statistical analyze methods 5 3 Make statistical inference(estimation, hypothesis tests etc.) 4 4 Generate solutions for the problems in other disciplines by using statistical techniques 4 5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0 6 Construct a model and analyze it by using statistical packages 5 7 Distinguish the difference between the statistical methods 4 8 Be aware of the interaction between the disciplines related to statistics 3 9 Make oral and visual presentation for the results of statistical methods 2 10 Have capability on effective and productive work in a group and individually 1 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 3 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 2 13 Emphasize the importance of Statistics in life 5 14 Define basic principles and concepts in the field of Law and Economics 0 15 Produce numeric and statistical solutions in order to overcome the problems 4 16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 5 17 Use proper methods and techniques to gather and/or to arrange the data 5 18 Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 0 * 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 0 0 0     Mid-term Exams (Written, Oral, etc.) 1 20 20     Final Exam 1 30 30 Total Workload: 134 Total Workload / 25 (h): 5.36 ECTS Credit: 5