This course deals with the basic theory and the recent development in time-series analysis, which will cover smoothing methods, Box-Jenkins type models such as moving average (MA) process, autoregressive (AR) process, ARMA, ARIMA, some state-space models. Multivariate time series such as VAR and ARCH/GARCH models will be also dealt with. Applications to economic and financial time series will be made.

Probability and Statistics

participation and homeworks 20%
midterm exam 20%
final exam 40%
term paper 20%

- Time Series Analysis by J. D. Hamilton, 1st Edition, Princeton University Press, 1994.
- Time Series Analysis: Forecasting and Control by G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, 3rd Edition, Prentice Hall, 1994.
- Statistical Methods for Forecasting by B. Abraham and J. Ledolter, 1st or 2nd Edition, Wiley-Interscience, 1983 or 2005.
- The Analysis of Time Series: An Introduction by Chatfield, C., Chapman & Hall, 1984.
- Forecasting Methods for Management by Makridakis, S. and S.T. Wheelwright, 5th edition, Wiley, 1989.
- Forecasting Structural Time Series Models and the Kalman Filter by Harvey, A.C., Cambridge Univ. Press, 1989.
- Forecasting in Business and Econometrics by Granger, C.W.J., 2nd edition, Academic Press, 1989.
- Bayesian Forecasting and Dynamic Models by West, M. and J. Harrison, Springer-Verlag, 1989.
- P.J. Brockwell and R.A. Davis, Introduction to Time Series and Forecasting, second edition, 2002, Springer.

- Introduction
- Smoothing methods
- Stationary processes: AR, MA, ARMA
- ARMA models
- Spectral Analysis
- Modeling and Forecasting with ARMA Processes
- Nonstationary and Seasonal Time Series Models: ARIMA
- Multivariate Time Series: VAR, Impulse Response Function, Cointegration/Error Correction Models
- State space models

- 강의유형: 강의실 (대면) 강의
- 수업방식: 강의, 숙제, term project, 시험