Bilinear time series models are of importance to nonlinear time seriesanalysis.In this paper,the autocovariance function and the relation between linearand general bilinear time series models are derived.With the help...Bilinear time series models are of importance to nonlinear time seriesanalysis.In this paper,the autocovariance function and the relation between linearand general bilinear time series models are derived.With the help of Volterra seriesexpansion,the impulse response function and frequency characteristic function of thegeneral bilinear time series model are also derived.展开更多
With a view to providing a tool to accurately model time series processes which may be corrupted with errors such as measurement,round-off and data aggregation,this study developedan integrated moving average(IMA)mode...With a view to providing a tool to accurately model time series processes which may be corrupted with errors such as measurement,round-off and data aggregation,this study developedan integrated moving average(IMA)model with a transition matrix for the errors resulting ina convex combination of two ARMA errors.Datasets on interest rates in the United States andNigeria were used to demonstrate the application of the formulated model.Basic tools such asthe autocovariance function,maximum likelihood method,Newton–Raphson iterative methodand Kolmogorov–Smirnov test statistic were employed to examine and fit the formulated specification to data.Test results showed that the proposed model provided a generalisation and amore flexible specification than the existing models of AR error and ARMA error in fitting timeseries processes in the presence of errors.展开更多
文摘Bilinear time series models are of importance to nonlinear time seriesanalysis.In this paper,the autocovariance function and the relation between linearand general bilinear time series models are derived.With the help of Volterra seriesexpansion,the impulse response function and frequency characteristic function of thegeneral bilinear time series model are also derived.
文摘With a view to providing a tool to accurately model time series processes which may be corrupted with errors such as measurement,round-off and data aggregation,this study developedan integrated moving average(IMA)model with a transition matrix for the errors resulting ina convex combination of two ARMA errors.Datasets on interest rates in the United States andNigeria were used to demonstrate the application of the formulated model.Basic tools such asthe autocovariance function,maximum likelihood method,Newton–Raphson iterative methodand Kolmogorov–Smirnov test statistic were employed to examine and fit the formulated specification to data.Test results showed that the proposed model provided a generalisation and amore flexible specification than the existing models of AR error and ARMA error in fitting timeseries processes in the presence of errors.
