Time Series Model Identification by Estimating Information.
TEXAS A AND M UNIV COLLEGE STATION INST OF STATISTICS
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Statisticians, economists, and system engineers are becoming aware that to identify models for time series and dynamic systems, information theoretic ideas can plan a valuable and unifying role. This paper discusses how models for a univariate or multivariate time series Yt can be formulated as hypotheses about the information divergence between alternative models for the conditional probability density of Yt given various bases involving past, current, and future values of Y. and related time series x.. To determine sets of variables that are sufficient to forecast Yt, and thus to determine a model for Yt, an approach is presented which estimates and compares various information increments. These information numbers play a central role in studies of causality and feedback. Approximating autoregressive schemes are used to form estimators of the many information numbers that one might compare to identify models for a time series.
- Statistics and Probability