| Dersin adı |
Dersin seviyesi |
Dersin kodu |
Dersin tipi |
Dersin dönemi |
Yerel kredi |
AKTS kredisi |
Ders bilgileri |
| ECONOMETRICS I |
İkinci düzey |
ECON 675 |
|
1 |
7.00 |
7.00 |
Yazdır |
|
Ön koşul dersleri
|
No
|
|
Eğitimin dili
|
English
|
|
Koordinatör
|
PROF. DR. FAİK BİLGİLİ
|
|
Dersi veren öğretim eleman(lar)ı
|
PROF. DR. FAİK BİLGİLİ
|
|
Yardımcı öğretim eleman(lar)ı
|
No
|
|
Dersin veriliş şekli
|
Lecture and Computer Applications
|
|
Dersin amacı
|
To teach the students the habit of thought, knowledge and understanding to be able to carry out good quality applied economic research with confidence To develop the critical insight to appraise econometric results obtained by other researchers.
|
|
Dersin tanımı
|
LS, MO Estimation methods. Tests for the main assumptions of LS method and etc.
|
|
1- |
Introduction to time-series models
|
|
2- |
Difference Equations and their solutions
|
|
3- |
Convergence, divergence, and stability of the equilibrium
|
|
4- |
Model selection (Box-Jenkins) and long-memory models (ARFIMA)
|
|
5- |
ARMA and ARIMA models .
|
|
6- |
Auto-correlation and partial autocorrelation functions
|
|
7- |
Stochastic difference models and stationarity and unit root tests
|
|
8- |
Midterm
|
|
9- |
Applications: Regression with time series errors, A model of interest rate spread
|
|
10- |
Forecast evaluation and structural break tests + Markov-regime switching
|
|
11- |
Characteristics of volatility and testing for ARCH effects
|
|
12- |
GARCH models
|
|
13- |
Applications to GARCH models
|
|
14- |
Alternative conditional variance models: T-GARCH, GARCH-M, E-GARCH, Asymmetric GARCH, and their applications
|
|
15- |
Course review and state of art methods (Fourier wavelet estimations)
|
|
16- |
Final Exams
|
|
17- |
Final Exams
|
|
18- |
|
|
19- |
|
|
20- |
|
|
1- |
By the end of the course the students will have developed the necessary skills needed for empirical research using modern econometrics techniques. To have knowledge on the basic principles of econometric analysis.
|
|
2- |
To be able to know how to apply regression analysis to real-world economic examples and data sets for hypothesis testing and prediction. Through their computer based assignments they will be also trained in conducting research using primary data
|
|
3- |
To have knowledge on simple and multiple regression models. To be able to understand the assumptions of the classical regression model.
|
|
4- |
To have knowledge on problems on estimation of models and to be able to understand both the fundamental techniques and wide array of applications involving linear regression estimation.
|
|
5- |
To develop the critical insight to appraise econometric results obtained by the other researchers
|
|
6- |
To have some knowledge on econometric computer programs such as Eviews and Stata
|
|
7- |
|
|
8- |
|
|
9- |
|
|
10- |
|
|
*Dersin program yeterliliklerine katkı seviyesi
|
|
1- |
|
|
|
2- |
|
|
|
3- |
|
|
|
4- |
|
|
|
5- |
|
|
|
6- |
|
|
|
7- |
|
|
|
8- |
|
|
|
9- |
|
|
|
10- |
|
|
|
11- |
|
|
|
12- |
|
|
|
13- |
|
|
|
14- |
|
|
|
15- |
|
|
|
16- |
|
|
|
17- |
|
|
|
18- |
|
|
|
19- |
|
|
|
20- |
|
|
|
21- |
|
|
|
22- |
|
|
|
23- |
|
|
|
24- |
|
|
|
25- |
|
|
|
26- |
|
|
|
27- |
|
|
|
28- |
|
|
|
29- |
|
|
|
30- |
|
|
|
31- |
|
|
|
32- |
|
|
|
33- |
|
|
|
34- |
|
|
|
35- |
|
|
|
36- |
|
|
|
37- |
|
|
|
38- |
|
|
|
39- |
|
|
|
40- |
|
|
|
41- |
|
|
|
42- |
|
|
|
43- |
|
|
|
44- |
|
|
|
45- |
|
|
|
Yıldızların sayısı 1’den (en az) 5’e (en fazla) kadar katkı seviyesini ifade eder |
|
Planlanan öğretim faaliyetleri, öğretme metodları ve AKTS iş yükü
|
|
|
Sayısı
|
Süresi (saat)
|
Sayı*Süre (saat)
|
|
Yüz yüze eğitim
|
14
|
3
|
42
|
|
Sınıf dışı ders çalışma süresi (ön çalışma, pekiştirme)
|
14
|
2
|
28
|
|
Ödevler
|
7
|
4
|
28
|
|
Sunum / Seminer hazırlama
|
0
|
0
|
0
|
|
Kısa sınavlar
|
0
|
0
|
0
|
|
Ara sınavlara hazırlık
|
1
|
20
|
20
|
|
Ara sınavlar
|
1
|
2
|
2
|
|
Proje (Yarıyıl ödevi)
|
0
|
0
|
0
|
|
Laboratuvar
|
0
|
0
|
0
|
|
Arazi çalışması
|
0
|
0
|
0
|
|
Yarıyıl sonu sınavına hazırlık
|
1
|
12
|
12
|
|
Yarıyıl sonu sınavı
|
1
|
2
|
2
|
|
Araştırma
|
5
|
3
|
15
|
|
Toplam iş yükü
|
|
|
149
|
|
AKTS
|
|
|
6.00
|
|
Değerlendirme yöntemleri ve kriterler
|
|
Yarıyıl içi değerlendirme
|
Sayısı
|
Katkı Yüzdesi
|
|
Ara sınav
|
1
|
40
|
|
Kısa sınav
|
0
|
0
|
|
Ödev
|
0
|
0
|
|
Yarıyıl içi toplam
|
|
40
|
|
Yarıyıl içi değerlendirmelerin başarıya katkı oranı
|
|
40
|
|
Yarıyıl sonu sınavının başarıya katkı oranı
|
|
60
|
|
Genel toplam
|
|
100
|
|
Önerilen veya zorunlu okuma materyalleri
|
|
Ders kitabı
|
Enders, W. (2008). Applied econometric time series. John Wiley & Sons.
|
|
Yardımcı Kaynaklar
|
R (2022), R Project manuals and documentations, https://cran.r-project.org/manuals.html
Mathworks, 2022, MATLAB documentation, https://www.mathworks.com/help/matlab/
Tsay, R. S. (2013). Multivariate time series analysis: with R and financial applications. John Wiley & Sons.
Tsay, R. S. (2005). Analysis of financial time series. John Wiley & sons.
Gujarathi, D. M. (2022). Gujarati: Basic Econometrics. McGraw-hill.
Hamilton, J. D. (2020). Time series analysis. Princeton university press.
|
|