Time-Varying Moving Average Model for Autocovariance Nonstationary Time Series (Journal of Fiber Bioengineering and Informatics, 2014)
In time series analysis, fitting the Moving Average (MA) model is more complicated than Autoregressive(AR) models because the error terms are not observable. This means that iterative nonlinear fittingprocedures need to be used in place of linear least squares. In this paper, Time-Varying Moving Average(TVMA) models are proposed for an autocovariance nonstationary time series. Through statisticalanalysis, the parameter estimates of the MA models demonstrate high statistical efficiency. The AkaikeInformation Criterion (AIC) analyses and the simulations by the TVMA models were carried out. Thesuggestion about the TVMA model selection is given at the end. This research is useful for analyzingan autocovariance nonstationary time series in theoretical and practical fields.
Keywords: MA Model; Autocovariance; Parameter Estimation; Simulation; Model Selection