Bayesian Analyses of Nonhomogeneous Autoregressive Processes
STANFORD UNIV CA DEPT OF STATISTICS
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This paper considers nonhomogeneous autoregressive processes which are special cases of the vector-valued autoregressive processes considered by Anderson 1978 for the analysis of panel survey data. The authors 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. Presented are two Bayesian approaches based on Kalman filter models which alleviate the above difficulty and result in an alternative strategy for the analyses of nonhomogeneous autoregressive processes. In the first approach the notion of exchangeability plays a key role, whereas for the second approach, which results in an adaptive Kalman filter model, an approximation due to Lindley facilitates the necessary computations for inference.
- Statistics and Probability