Advanced Time Series

Paper Code: 
STT 143(C)
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

To introduce a variety of statistical models for time series and cover the main methods for analysing these models.

12.00

UNIT I

Review of Linear Models: Model building in time series analysis. AR, MA, ARMA and ARIMA model and model building by Box-Jenkins approach. Stationarity and invertibility conditions, ARIMA(p,d,q) model, estimation of parameters for AR, MA, ARMA and ARIMA processes, identification of processes with ACF PACF, Model order estimation techniques-AIC, AICC, BIC, EDC, FPE and forecasting.

12.00

UNIT II

Forms of non-stationarity in time series, Unit root: Dickey-Fuller, augmented Dickey-Fuller and Phillips-Perron tests. Panel data models: Balance and unbalance panel data, estimation in random effect and fixed effect models. ARCH and GARCH processes and models with ARCH, GARCH errors.

12.00

UNIT III

Multivariate time series processes and their properties:  Vector autoregressive (VAR), vector moving average (VMA) and vector autoregressive moving average (VARMA) processes.

12.00

UNIT IV

Non Linear Time Series Models: Non-linear auto regression, Threshold principle and threshold models. Amplitude-dependent exponential autoregressive (EXPAR), fractional autoregressive (FAR), Product Autoregressive Model (PAR), random coefficient autoregressive (RCAR), discrete state space auto regressive bilinear (BL), non-linear moving average, autoregressive models with conditional heteroskedasticity (ARCH).

12.00

UNIT V

Non-linear Least Square Prediction: Non-linear least square prediction in non-linear autoregressive, nonlinear moving average, Bilinear and random coefficient autoregressive models

Essential Readings: 
 
  • Box, G.E.P. and G.M. Jenkins. Time Series Analysis, Forecasting and Control.
  • Brockwell, P.J. and Davis, R.A.. Time Series: Theory and Methods (Second Edition), Springer-Verlag.
  • Chatfield, C.: The Analysis of Time Series: Theory and Practice.
  • Granger, C.W.J. and Newbold, Forecasting Econometric Time Series, Third Edition, Academic Press.
  • Kirchgassner, G. and Wolters,J. Introduction to Modern Time Series Analysis, Springer.
  • Montgomery, D.C. and Johnson, L.A. Forecasting and Time Series Analysis, McGraw Hill.
  • William W S W: Time Series Analysis : Univariate and Multivariate Gregory C, Elements of Multivariate Time
  • Series Analysis, Springer Series in Statistics
  • Hamilton, J D: Time Series Analysis, Princeton University Press
  • Harvey, A C: Time Series Models
  • Harvey, A C: The Econometric Analysis of Time Series, Pearson education,
  • Enders W, Applied Econometric Times Series, Wiley Series in Probability and Statistics,
  • Tsay R S, Analysis of Financial Time Series, (Wiley Series in Probability and Statistics)
  • Tong, H. (1990): Non-linear time series, A dynamical system approach, Oxford
  • Priestly, M.B. (1988): Non-linear and non-stationary time series analysis, Academic Press, London.

 

Academic Year: 
2022-2023
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