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Information
| Unit | FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES |
| ECONOMETRICS PR. | |
| Code | EKMZ405 |
| Name | Time Series Models I |
| Term | 2019-2020 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 |
Doç. Dr. ÇİLER SİGEZE GÜNEY
(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 | - | Explains Econometric concepts | 5 |
| PLO02 | - | Acquires basic Mathematics, Statistics and Operation Research concepts | 5 |
| PLO03 | - | Equipped with the foundations of Economics, and develops Economic models | 4 |
| PLO04 | - | Describes the necessary concepts of Business | 1 |
| PLO05 | - | Models problems with Mathematics, Statistics, and Econometrics | 5 |
| PLO06 | - | Estimates the model consistently and analyzes & interprets its results | 5 |
| PLO07 | - | Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems | 5 |
| PLO08 | - | Collects, edits, and analyzes data | 4 |
| PLO09 | - | Uses a package program of Econometrics, Statistics, and Operation Research | 1 |
| PLO10 | - | Effectively works, take responsibility, and the leadership individually or as a member of a team | 2 |
| PLO11 | - | Awareness towards life-long learning and follow-up of the new information and knowledge in the field of study | 4 |
| PLO12 | - | Develops the ability of using different resources in the form of academic rules, synthesis the information gathered, and effective presentation in an area which has not been studied | 2 |
| PLO13 | - | Uses Turkish and at least one other foreign language, academically and in the business context | 2 |
| PLO14 | - | Good understanding, interpretation, efficient written and oral expression of the people involved | 2 |
| PLO15 | - | Improves himself/herself constantly by defining educational requirements considering interests and talents in scientific, cultural, art and social fields besides career development | 1 |
| PLO16 | - | Questions traditional approaches and their implementation while developing alternative study programs when required | 4 |
| PLO17 | - | Recognizes and implements social, scientific, and professional ethic values | 4 |
| PLO18 | - | Follows actuality, and interprets the data about economic and social events | 4 |
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 | ||