Optimal Estimation of Autoregressive Models with Non-stationary Innovations: A simulation study
by K.H. Ng and M.S. Peiris
In many applications of time series, the assumption of stationarity has been widely used to analyse and model time series data. However, this assumption is too restrictive for many real data sets. Therefore, this paper investigates the properties of the class of first order autoregressive (AR(1)) models with constant coefficients and non-stationary innovations (see  for details) and considers the parameter estimation in details. The theory of estimating functions (EF) has been reviewed and applied to obtain the corresponding optimal estimates of the respective parameters. A large scale simulation study is carried out to investigate the performance of the resulting estimates. It is shown that the innovation variance affect the estimation of the AR parameter in some cases.
AR models, Non-stationary Innovations, Bootstrap, Optimal estimation, Simulations, Efficiency, Estimating Function, Modelling
K.H. Ng, firstname.lastname@example.org
M.S. Peiris, email@example.com
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