On Time Series Model Selection Involving Very Many Candidate ARMA Models
by Dr Guoqi Qian
Abstract: We study how to perform model selection for time series data
where millions of candidate ARMA models may be eligible for selection.
We propose a feasible computing method based on the Gibbs sampler.
By this method model selection is performed through a random sample generation algorithm, and given a model of fixed dimension the parameter
estimation is done through the maximum likelihood method. Our method takes into account several computing difficulties encountered in estimating ARMA models. The method is found to have probability of 1 in the limit in selecting the best candidate model under some regularity conditions. We then propose several empirical rules to implement our computing method for applications. Finally a simulation study and an example on modelling China's Consumer Price Index (CPI) data are presented for purpose of illustration and verification.
For More Information: Dr Owen Jones: email@example.com