Time Series Analysis

Paper Code: 
STT 422(C)
Credits: 
5
Contact Hours: 
75.00
Max. Marks: 
100.00
Objective: 

This paper is design to help the students in the field of forecasting and monitoring the data points by applying suitable model to time series data

 

Course

Learning outcomes

(at course level)

Learning and teaching strategies

Assessment

Strategies

Paper Code

Paper Title

STT

422(C)

Time Series Analysis (Theory)

 

The students will be able to –

 

CO97: Define time series components and their uses

CO98: Able to construct stationary time series models, nonlinear stochastic models and their applications

CO99: Students will demonstrate their ability to apply statistics in other fields at an appropriate level and demonstrate their ability to apply knowledge acquired from their major to real world models.

 

Approach in teaching:

 

Interactive Lectures, Discussion, Power Point Presentations, Informative videos

 

Learning activities for the students:

Self learning assignments, Effective questions, presentations, Field trips

 

 

Quiz, Poster Presentations,

Power Point Presentations, Individual and group projects,

Open Book Test, Semester End Examination

 

 

 

 

15.00

Time Series Analysis- Definition and its different components, additive and multiplicative models. Different methods of determining trend and seasonal and cyclic fluctuations, their merits and demerits. Time series as discrete parameter stochastic process, auto-covariance and auto-correlation functions and, their properties.

 

 

15.00

Detailed study of the stationary processes: (i) moving average (MA), (ii) auto-regressive (AR),(iii) ARMA, and, (iv) AR integrated MA (ARIMA) models. Box-Jenkins models. Discussion (withoutproof) of estimation of mean, auto-covariance and auto-correlation functions under large sample theory.Choice of AR and MA orders. Estimation of ARIMA model parameters

 

 

15.00

Spectral analysis of weakly stationary process, peridogram and correlogram analyses, computations based on Fourier transform. Forecasting: Exponential and adaptive Smoothing methods`

 

15.00

Multivariate Liner Time series: Introduction, Cross covariance and correlation matrices, testing of zero cross correlation and model representation. Basic idea of Stationary vector Autoregressive Time Series with orders one: Model Structure, Granger Causality, stationary condition, Estimation, Model checking.

 

 

15.00

Non-linear time series models, ARCH and GARCH Process, order identification, estimation and diagnostic tests and forecasting. Study of ARCH (1) properties. GARCH (Conception only) process for  modelling volatility.

 

Essential Readings: 
  • Box, G.E.P. and Jenkins, G.M. (1976): Time series analysis—Forecasting and Control, Holden-day, San Francisco.
  • Anderson, T.W. (1971): The Statistical Analysis of Time Series, Wiley, N.Y.
  • Montgemory, D.C. and Johnson, L.A. (1977): Forecasting and Time Series Analysis, McGraw Hill.
  • Kendall, Sir Maurice and Ord, J.K. (1990): Time Series (Third Edition), Edward Arnold.
  • Brockwell, P.J. and Davis, R.A.: Time Series: Theory and Methods (Second Edition), Springer- Verlag.
  • Fuller, W.A. (1976): Introduction to Statistical Time Series, John Wiley, N.Y.
  • Granger, C.W.J. and Newbold (1984): Forecasting Econometric Time Series, Third Edition, Academic Press.
  • Priestley, M.B. (1981): Spectral Analysis & Time Series, Griffin, London.
  • Kendall, M.G. and Stuart A. (1966): The Advanced Theory of Statistics, Volume 3, Charles Griffin, London.
  • Bloomfield, P. (1976): Fourier Analysis of Time Series—An Introduction, Wiley.
  • Granger, C.W.J. and Hatanka, M. (1964): Spectral Analysis of Economic Time Series, Princeton Univ. Press, N.J.
  • Koopmans, L.H. (1974). The spectral Analysis of Time Series, Academic Press.
  • Nelson, C.R. (1973): Applied Time Series for Managerial Forecasting, Holden-Day.
  • Findley, D.F. (Ed.) (1981): Applied Time Series Analysis II, Academic Press.

 

Academic Year: 
2020-2021
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