Time Series Analysis and Macroeconometric Applications
ataozk@yahoo.com
Content
Objective
This course aims to provide graduate students with the advanced theoretical foundations of time series econometrics and their applications in macroeconomics. Ranging from the modeling of stationary and non-stationary time series to multivariate time series analysis (VAR, cointegration, VECM) and advanced topics such as structural breaks and state-space models, the course equips students with the analytical skills needed to conduct original macroeconometric research.
Course Content
The course covers stationarity and unit root tests, ARIMA models, multivariate time series models (VAR, structural VAR), cointegration analysis (Engle-Granger and Johansen methods), and error correction models (VECM). It also addresses conditional heteroskedasticity models (the ARCH/GARCH family), an introduction to panel time series methods, structural break tests, and an introduction to state-space models and the Kalman filter. All methods are addressed in an applied manner using macroeconomic datasets.
Course Learning Outcomes
Analyze non-stationary time series using unit root tests (ADF, PP, KPSS)
Construct ARIMA and seasonal ARIMA models and generate forecasts for macroeconomic series
Construct VAR and structural VAR models; interpret impulse response functions and variance decomposition
Test for cointegration using the Engle-Granger and Johansen methods, and construct a VECM
Model the conditional heteroskedasticity of macroeconomic/financial series using ARCH/GARCH family models
Apply structural break tests to assess model stability
Explain the fundamental logic of state-space models and the Kalman filter approach
Design and conduct applied macroeconometric research using econometric software (EViews, R, Python, Stata, etc.)
Present findings in a research report or paper that conforms to academic writing standards
Teaching and Learning Methods
Lectures: advanced presentation of the theoretical and mathematical foundations of the methods
Lab/application sessions: model building and estimation on real macroeconomic data using software (EViews, R, Python, or Stata)
Academic paper presentations: presentation and discussion of recent empirical studies in the field by students
Weekly/periodic homework: independent application of the methods covered
Term project/research assignment: econometric analysis of a macroeconomic question chosen by the student
Office hours: individual research consultation
Assessment: midterm exam/presentation, homework, and term project/final exam
References
J.D. Hamilton "Time Series Analysis" Princeton 1994
F. Hayashi " Ecoonmetrics" Princeton, 2001
Kantz&Schreiber 2004 "Nonlinear Time Series Analysis"
Gilboa, Decision Making under risk and uncertainty, lecture notes
Macroeconomics, Blanchard
Money, Banking and Economy of Finance, Mishkin 2011
Optimisation- Belmann , Chiang 1997
R
Weekly Contents
| Week | Weekly Contents |
|---|---|
| 1 | Stochastic Modeling of Risk, Uncertainty and Financial Prices |
| 2 | Financial Time Series: Stagnation, Dependency Structures and Model Specification |
| 3 | Vector Autoregressive (VAR) Models: Theoretical Foundations, Prediction and Diagnostic Test |
| 4 | Dynamic Analysis of VAR Models: Granger Causality, Impulse Response Functions, and Variance Decomposition |
| 5 | Ecomomy political analysis * pc programming and statistical tools |
| 6 | Complex Financial Systems: Leverage, Liquidity, Network Interconnects and Systemic Risk |
| 7 | |
| 8 | |
| 9 | |
| 10 | |
| 11 | |
| 12 | |
| 13 | |
| 14 |
Assessment System
| Activities | Number | Contribution |
|---|---|---|
| Contribution of in-term studies to overall grade | 4 | 60 |
| Contribution of final exam to overall grade | 2 | 40 |
| Total | 6 | 100 |
| Activities | Number | Contribution |
|---|---|---|
| Assignments | 4 | 60 |
| Presentation | 2 | 40 |
| Midterm Examinations (including preparation) | 0 | 0 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Make-up | 0 | 0 |
| Total | 6 | 100 |
ECTS
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 3 | 13 | 39 |
| Working Hours out of Class | 3 | 3 | 9 |
| Assignments | 5 | 8 | 40 |
| Presentation | 3 | 6 | 18 |
| Midterm Examinations (including preparation) | 1 | 1 | 1 |
| Project | 3 | 9 | 27 |
| Laboratory | 3 | 4 | 12 |
| Other Applications | 1 | 2 | 2 |
| Final Examinations (including preparation) | 1 | 3 | 3 |
| Quiz | 1 | 3 | 3 |
| Term Paper/ Project | 2 | 2 | 4 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 5 | 4 | 20 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 3 | 3 | 9 |
| Other | 0 | 0 | 0 |
| Make-up | 1 | 3 | 3 |
| Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Bütünleme | 0 | 0 | 0 |
| Total Workload | 190 | ||
| Total Workload / 25 | 7.60 | ||
| Credits ECTS | 8 | ||