Stanford, California Approved for Public Release; Distribution Unlimited, Bayesian Analyses of Nonhomogeneous Autoregressive Processes
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چکیده
In this paper we consider nonhomogeneous autoregressive processes which are special cases of the vector-valued autoregressive processes considered by Anderson (1978) for the analysis of panel survey data. We point out that, for a nonhomogeneous autoregressive process of order higher than one, the least-squares estimates cannot be obtained unless repeated measurements are made on the time series. We present here two Bayesian approaches based on Kaiman filter models which alleviate the above difficulty and result in an alternative strategy for the analyses of nonhomogeneous autoregressive processes. In our first approach the notion of exchangeability plays a key role, whereas for our second approach, which results in an adaptive Kaiman filter model, an approximation due to Lindley facilitates the necessary computations for inference.
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