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Information
| Unit | FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES |
| ECONOMETRICS PR. | |
| Code | EKMZ405 |
| Name | Time Series Models I |
| Term | 2017-2018 Academic Year |
| Semester | 7. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi FELA ÖZBEY |
| Course Instructor |
Dr. Öğr. Üyesi FELA ÖZBEY
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim of this course is to give the students a good theoretical and empirical understanding of statistical methods used in univariate time series analysis.
Course Content
Stochastic process and time series concepts. Analysis of time series: time series analysis in time domain, time series analysis in frequency domain. Components of economic time series. Difference equations: Stability of difference equations, Impulse-response function. Expectations of processes, stationarity, and ergodicity. Trend stationary and difference stationary processes. White noise process, MA(q) processes, AR(p) processes, Random walk process, ARIMA(p,d,q) processes. Invertibility for MA(q) processes. Overparametrization of the ARMA models. The Box-Jenkins method of ARIMA model identification. Autocorrelation and partian autocorrelation functions of AR, MA, and ARMA processes. Autoregressive Conditional Heteroskedasticity Models: ARCH, GARCH, TARCH, EGARCH, IGARCH, ARCH-M models. Autoregressive Regime-switching models: TAR, SETAR, ESTAR, LSTAR models.
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Defines Stochastic process and time series concepts. |
| LO02 | Analyzes difference equations, stability conditions of difference equations, and dynamic multipliers. |
| LO03 | Distinguishes between trend stationary and difference stationary process. |
| LO04 | Applies the Box-Jenkins method to univariate time series. |
| LO05 | Chooses the most appropriate model for the underlying univariate time series |
| LO06 | Chooses the most appropriate autoregressive conditional heteroskedastisity model for the single equation models. |
| LO07 | Recognizes random walk and white noise processes. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain the basic concepts and theorems in the fields of Econometrics, Statistics and Operations research | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Acquires basic Mathematics, Statistics and Operation Research concepts | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Describes the necessary concepts of Business | |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Equipped with the foundations of Economics, and develops Economic models | 2 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Analyzes/interprets at the conceptual level to develop solutions to problems | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Collects/analyses data from reliable data sources for the purpose of study | 5 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Interprets the results analyzed with the model | 5 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Combines the information obtained from different sources within the framework of academic rules in a field of research | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Adapts traditional approaches, practices and methods to a new study when necessary | 2 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses a package program of Econometrics, Statistics, and Operation Research | |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | In addition to herself/himself professional development, constantly improves in scientific, cultural, artistic and social fields in line with interests and abilities | |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in its field. | 2 |
| PLO15 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses Turkish and at least one other foreign language, academically and in the business context | 2 |
| PLO16 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form | |
| PLO17 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on current economic and social issues | 3 |
| PLO18 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Stochastic process and time series concepts. Analysis of time series: time series analysis in time domain, time series analysis in frequency domain. Components of economic time series. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 2 | First-order difference equations: Definition, Solving a difference equation by recursive substitution, stability offirst-order difference equations, Impulse-response function. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 3 | pth-order difference equations: Definition, Solving a difference equation by recursive substitution, stability offirst-order difference equations, Impulse-response function. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 4 | Lag operator, Differencing operator. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 5 | Expectations of processes, stationarity, and ergodicity. Trend stationary and difference stationary processes. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 6 | White noise process, MA(q) processes, | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 7 | AR(p) processes, Random walk process, ARIMA(p,d,q) processes. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 8 | Midterm Exam | ||
| 9 | Invertibility for MA(q) processes. Overparametrization of the ARMA models. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 10 | The Box-Jenkins method of ARIMA model identification. Autocorrelation and partian autocorrelation functions of AR, MA, and ARMA processes. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 11 | Autoregressive Conditional Heteroskedasticity Models: ARCH and GARCH models | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 12 | Autoregressive Conditional Heteroskedasticity Models: TARCH, EGARCH, IGARCH, ARCH-M models | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 13 | Autoregressive Regime-switching models: TAR, SETAR, ESTAR, LSTAR models. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 14 | Autoregressive Regime-switching models: Choosing the most appropriate model. | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 15 | An overview | Students will be prepared by studying relevant subjects from source books according to the weekly program | |
| 16 | Final Exam | ||
| 17 | Final Exam |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 10 | 10 |
| Total Workload (Hour) | 86 | ||
| Total Workload / 25 (h) | 3,44 | ||
| ECTS | 3 ECTS | ||