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date: 05 April 2020

Abstract and Keywords

This chapter deals with time domain statistical models and methods on analyzing time series and their use in applications. It covers fundamental concepts, stationary and nonstationary models, nonseasonal and seasonal models, intervention and outlier models, transfer function models, regression time series models, vector time series models, and their applications. We discuss the process of time series analysis including model identification, parameter estimation, diagnostic checks, forecasting, and inference. We also discuss autoregressive conditional heteroscedasticity model, generalized autoregressive conditional heteroscedasticity model, and unit roots and cointegration in vector time series processes.

Keywords: Autoregressive model, moving average model, autoregressive moving average model, autoregressive integrated moving average model, intervention, outlier, transfer function model, autoregressive conditional heteroscedasticity model, generalized autoregressiv

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