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Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models
2020
Bayesian Analysis
We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic modeling of multivariate time series. The perspective is that of a decision-maker with a specific forecasting objective that guides thinking about relevant models. Based on formal Bayesian decision-theoretic reasoning, we develop a time-adaptive approach to exploring, weighting, combining and selecting models that differ in terms of predictive variables included. The adaptivity allows for changes in the sets of
doi:10.1214/20-ba1245
fatcat:4t7pu6w6onedbbdh7slpddql5y