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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 6062 NONPARAMETRIC MODELS ELECTIVE 3 0 0 7

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR DOCTOR RABIA ECE OMAY

Offered to

Econometrics

Course Objective

The aim of the course is to construct nonparametric, semiparametric, additive and generalized additive models using a regression spline, interpret the results and compare them with other regression models.

Learning Outcomes of the Course Unit

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 1 Roughness penalty approach 2 2 Interpolation and smoothing splines 3 3 Smoothing parameter selection, cross validation and generalized cross validation 4 4 Penalized least squares method for partial parametric models 5 5 Additive models 6 6 Generalized linear models 7 7 Generalized additive models 8 8 Generalized additive models 9 9 Solution algorithms of generalized additive models 10 10 Selection of smoothing parameters, degrees of freedom 11 11 Applications of additive and generalized additive regression models in R with spline regression 12 12 Applications of additive and generalized additive regression models in R with spline regression 13 13 Applications of additive and generalized additive regression models in R with spline regression 14 14 Applications of additive and generalized additive regression models in R with spline regression

Recomended or Required Reading

Hastie, T.J., Tibshirani, R.J. (1999) Generalized Additive Models, Chapman&Hall/CRC, NewYork. Buja, A., Hastie, T., Tibshirani, R. (1989) Linear Smoothers and Additive Models, The Annals of Statistics, Vol.17, No.2, 453-555. Wahba, G. (1990) Spline Models of Observational Data, SIAM, Pennsylvania.

Planned Learning Activities and Teaching Methods

1. Lecture 2. Show with applications 3. Discussion and analysis of identified cases

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 * 0.20 + STT * 0.30 + FIN* 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.30 + 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