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Activity Number: 427 - SPEED: Bayesian Methods, Part 2
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 3:05 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #307858
Title: Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models
Author(s): Isaac Lavine* and Michael Lindon and Mike West
Companies: Duke University and Tesla and Duke University
Keywords: Bayesian Forecasting; Decision Theory; Time Series; Variable Selection

We tackle the problem of model uncertainty and forecasting in the setting of on-line Bayesian analysis of multivariate time series. We adopt the perspective of a decision-maker with a specific objective function. An adaptive, Bayesian decision-guided approach is developed to select models for this purpose. Forecasts are made within a theoretically motived Bayesian model averaging framework. A simulation study illustrates how decision-guided variable selection differs from likelihood-based Bayesian model averaging. A case study is developed for long-term forecasting of macroeconomic data, where the only predictors are lagged values. The methodology reveals that the incorporation of older lagged values improves long-term macroeconomic forecasting.

Authors who are presenting talks have a * after their name.

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