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Information
| Code | IEM1846 |
| Name | Applied Time Series Models II |
| Semester | . Semester |
| Duration (T+A) | 4-0 (T-A) (17 Week) |
| ECTS | 8 ECTS |
| National Credit | 4 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi FELA ÖZBEY |
Course Goal
The aim of this course is to introduce methods modelling spatial autocorrelations in regrresion analysis, and R programming language.
Course Content
The Classical Linear Regression Model, Some Important Spatial Definitions, Spatial Linear Regression Models, applications with R programming language.
Course Precondition
None
Resources
Robert H. Shumway, David S. Stoffer (2011),Time Series Analysis and its Applications with R Examples, Third Edition, Springer-Verlag, New York, ISBN 978-1-4419-7864-6
Notes
James Douglas Hamilton, (1994) Time Series Analysis, Princeton University Press, ISBN: 9780691042893
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Specifies time series relations. |
| LO02 | Chooses the most appropriate model for time series data. |
| LO03 | Estimates time series models. |
| LO04 | Codes techniques and models taught in this course. |
| LO05 | Uses R programming language fluently. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Identify an econometric problem and propose a new solution to it | 2 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Develops new knowledge using current concepts in Econometrics, Statistics and Operations Research | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explain for what purpose and how econometric methods are applied to other fields and disciplines | 3 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Using her knowledge, brings original solutions to problems in Economics, Business Administration and other social sciences | 3 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Creates a new model using mathematics, statistics and econometrics knowledge to solve the problem encountered | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Performs conceptual analysis to develop solutions to problems | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Collects data on purpose | 5 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Synthesizes the information obtained by using different sources within the framework of academic rules in a field that does not research | 3 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | 2 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Converts its findings into a master's thesis or a professional report in Turkish or a foreign language | |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | It researches current approaches and methods to solve the problems it encounters and proposes new solutions | 2 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops long-term plans and strategies using econometric and statistical methods | |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs self-study using knowledge of Econometrics, Statistics and Operations to solve a problem | 4 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads the team by taking responsibility | |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in the field of study | 2 |
| PLO17 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution | |
| PLO18 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses a package program of Econometrics, Statistics, and Operation Research or writes a new code | 5 |
| PLO19 | 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 | |
| PLO20 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values | |
| PLO21 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on economic and social events by following current issues | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | FORECASTING: Minimum Mean Square Error Forecasting; Deterministic Trends Forecasting; ARIMA Forecasting; Prediction Limits; Forecasting Illustrations; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 2 | FORECASTING: Updating ARIMA Forecasts; Forecast Weights and Exponentially Weighted Moving Averages; Forecasting Transformed Series; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 3 | FORECASTING: Conditional Expectation; Minimum Mean Square Error Prediction; The Truncated Linear Process; State Space Models; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 4 | SEASONAL MODELS: Seasonal ARIMA Models; Multiplicative Seasonal ARMA Models; Nonstationary Seasonal ARIMA Models; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 5 | SEASONAL MODELS: Model Specification, Fitting, and Checking; Forecasting Seasonal Models; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 6 | TIME SERIES REGRESSION MODELS: Intervention Analysis; Outliers; Spurious Correlation; Prewhitening and Stochastic Regression; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 7 | TIME SERIES MODELS OF CONDITIONAL HETEROSCEDASTICITY: Some Common Features of Financial Time Series; GARCH Models; Maximum Likelihood Estimation; Model Diagnostics; Conditions for the Nonnegativity of the Conditional Variances; Some Extensions of the GARCH Model; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Ödev |
|
| 9 | INTRODUCTION TO SPECTRAL ANALYSIS: Introduction; The Periodogram; The Spectral Representation and Spectral Distribution; The Spectral Density; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 10 | INTRODUCTION TO SPECTRAL ANALYSIS: Spectral Densities for ARMA Processes; Sampling Properties of the Sample Spectral Density; Orthogonality of Cosine and Sine Sequences; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 11 | ESTIMATING THE SPECTRUM: Smoothing the Spectral Density ; Bias and Variance; Bandwidth; Confidence Intervals for the Spectrum; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 12 | ESTIMATING THE SPECTRUM: Leakage and Tapering; Autoregressive Spectrum Estimation; Examples with Simulated Data; Examples with Actual Data; Other Methods of Spectral Estimation; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 13 | THRESHOLD MODELS: Graphically Exploring; Tests for Nonlinearity; Polynomial Models Are Generally Explosive; First-Order Threshold Autoregressive Models; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 14 | THRESHOLD MODELS: Threshold Models; Testing for Threshold Nonlinearity; Estimation of a TAR Model; R applications. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 15 | Applications on Some Time Series | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Gösterip Yaptırma |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 8 | 112 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 4 | 8 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 24 | 24 |
| Total Workload (Hour) | 212 | ||
| Total Workload / 25 (h) | 8,48 | ||
| ECTS | 8 ECTS | ||