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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 6075 DISCRETE AND CONTINUOUS STATISTICAL DISTRIBUTIONS FOR ESTIMATION MODELS ELECTIVE 3 0 0 6

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

Offered to

Econometrics

Course Objective

Upon completion of this course, students ara expected to understand and apply bacis consepts related with the discrete and continuous statistical distributions and two important methods of parameter estimation. In particular, students will be able to understand consepts in maximum likelihood estimation and in the method of moments for parameter estimation.

Learning Outcomes of the Course Unit

1   To be able to compare the concepts of probability and likelihood 2   To be able to understand and to use consepts of sampling distributions and some important joint distributions commonly used in area of statistical area 3   To be able to understand and evaluate properties of point estimators, i.e., unbiasedness, efficiencyi aysmptotic efficiency, consistency and sufficiency 4   To be able to enchance their research and analytical thinking skills in the area of statistical inference

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Discrete and continuous random variables 2 Probability mass functions, probability density functions, cumulative distribution functions 3 Bernoulli Distribution, Binomial distribution, Negatice Binomial Distribution, Probability Generating Functions and Moment Generating Functions of these Distributions 4 Geometric Distribution and Negative Binomial Distribution, Probability Generating Functions and Moment Generating Functions of these Distributions 5 Continuous Uniform Distribution and Exponential Distribution, Moment Generating Functions of these Distributions 6 The Normal Distribution, The Standart Normal Distribution and The Gamma Distribution 7 Midterm 8 The concept of the parameter estimation. One-parameter estimation vs. multi-parametere estimation 9 Maximum Likelihood Estimation, applications for some discrete and continuous distributions 10 Properties of Maximum Likelihood Estimators 11 Fisher Informaiton, Cramer- Rao Lower Bound, Neyman-Pearson Theorem 12 The Methods Of Moments in Parameter Estimation, applications for some discrete and continuous distributions 13 The Methods Of Moments in Parameter Estimation, applications for some discrete and continuous distributions 14 Multi-parameter estimation in the Maximum Likelihood Technique. Applicatioc to the Gaussian Distributions

Recomended or Required Reading

1- George Casella ( Universtiy Of Florida ) and Roger L. Berger ( North Carolina State University ) Second Edition, 2002.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.20 + FIN* 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.20 + RST* 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) TOTAL WORKLOAD (hours) 0

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 LO.1 1 1 LO.2 1 1 LO.3 1 1 1 LO.4 1 1 1