PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
EKO 608	TIME SERIES ANALYSIS	3 + 0	2nd Semester	10

COURSE DESCRIPTION

Course Level	Doctorate Degree
Course Type	Elective
Course Objective	The aim of this lecture is to introduce to time series econometrics.
Course Content	Difference Equations,Lag Operators,Stationary ARMA Process,Forecasting,Maximum Likelihood Method,Spectral Analysis,Asymptotic Distribution Theory,Linear Regreession Models, Simultaneous Equations,Covariance-Stationary Vector Process,Vector Autoregression Models,Bayesian Models,Kalman Filter,Generalized Moments Methods,Non-Linear Time Series Models,Models with Deterministic Trend,Unit Root Tests,Cointegration,Heteroscedasticity's Time Series Models,
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Understanding seasonality properties of time series
2	Stationarity and Unit Root Analysis
3	VAR Models
4	Cointegration
5	Causality

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11	PO 12
LO 001	5	5	5	5	5	5	5	5	5	5	5	5
LO 002	5	5	5	5	5	5	5	5	5	5	5	5
LO 003	5	5	5	5	5	5	5	5	5	5	5	5
LO 004	5	5	5	5	5	5	5	5	5	5	5	5
LO 005	4	4	4	4	4	4	5	5	5	5	5	5
Sub Total	24	24	24	24	24	24	25	25	25	25	25	25
Contribution	5	5	5	5	5	5	5	5	5	5	5	5

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	6	84
Assignments	5	13	65
Mid-terms	1	30	30
Final examination	1	39	39
Total Work Load ECTS Credit of the Course			260 10

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2020-2021 Fall	1	MEHMET İVRENDİ

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
EKO 608	TIME SERIES ANALYSIS	3 + 0	1	Turkish	2020-2021 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. MEHMET İVRENDİ	mivrendi@pau.edu.tr		İİBF A0202	%70

Goals		The aim of this lecture is to introduce to time series econometrics.
Content		Difference Equations,Lag Operators,Stationary ARMA Process,Forecasting,Maximum Likelihood Method,Spectral Analysis,Asymptotic Distribution Theory,Linear Regreession Models, Simultaneous Equations,Covariance-Stationary Vector Process,Vector Autoregression Models,Bayesian Models,Kalman Filter,Generalized Moments Methods,Non-Linear Time Series Models,Models with Deterministic Trend,Unit Root Tests,Cointegration,Heteroscedasticity's Time Series Models,

Topics

Weeks	Topics
1	Introduction I
2	Introduction II
3	AR(p) Models
4	MA(q) Models
5	Model Selection in ARMA(p,q) Processes I
6	Model Selection in ARMA(p,q) Processes II
7	Stationarity and Invertibility
8	Mid-Term Exam
9	Non-stationarity and ARIMA(p,d,q) Processes I
10	Non-stationarity and ARIMA(p,d,q) Processes
11	Seasonal ARMA(p,q) Processes . .
12	Unit Root Tests I
13	Unit Root Tests II
14	Structural Breaks

Materials

Materials are not specified.

Resources

Resources	Resources Language
1-Walter Enders (2015) Applied Econometrics Time Series, Wiley	English
John D. Levendis (2018) Time Series Econometrics Learning Through Replication, Springer	English

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

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

Prospective Student