PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
EKNM 416	FORECASTING TECHNIQUES	3 + 0	8th Semester	5

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Elective
Course Objective	The general purpose of this course is to provide students with an understanding of the forecasting methods.
Course Content	Topics covered will include fundamentals of forecasting techniques; univariate, single equation and multiple equation forecasting models, selection of the best one between different forecasting models. Using computer programs and making applications with time series datas are an integral part of the course .
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Own sufficient knowledge about forecasting criteria and selecting models.
2	Can do forecasting with VAR models.
3	Can do forecasting with ARCH and GARCH models.
4	Can do forecasting with ARIMA models.
5	Own sufficient knowledge about basic forecasting techniques.

COURSE'S CONTRIBUTION TO PROGRAM

Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Mid-terms	1	23	23
Final examination	1	23	23
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
EKNM 416	FORECASTING TECHNIQUES	3 + 0	1	Turkish	2022-2023 Spring

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Assoc. Prof. Dr. SİNEM GÜLER KANGALLI UYAR	skangalli@pau.edu.tr		İİBF C0217	%70

Goals		The general purpose of this course is to provide students with an understanding of the forecasting methods.
Content		Topics covered will include fundamentals of forecasting techniques; univariate, single equation and multiple equation forecasting models, selection of the best one between different forecasting models. Using computer programs and making applications with time series datas are an integral part of the course .

Topics

Weeks	Topics
1	Some notation and concepts
2	Autoregressive processes
3	Deriving the properties of AR models
4	Moving average processes
5	Deriving the autocorrelation function for an MA process
6	The partial autocorrelation function
7	ARMA processes
8	the Box–Jenkins approach
9	Forecasting with R
10	Applications
11	Applications
12	Applications
13	Applications
14	Applications

Materials

Materials are not specified.

Resources

Resources	Resources Language
Ruppert, D., & Matteson, D. S. (2011). Statistics and data analysis for financial engineering (Vol. 13). New York: Springer.	English
Aljandali, A., & Tatahi, M. (2018). Economic and financial modelling with eviews. A Guide for Students and Professionals. Switzerland: Springer International Publishing.	Türkçe

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	60	Final Exam
Midterm Exam	40	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student