# Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models

## TL;DR

This paper introduces a Bayesian, decision-guided, time-adaptive variable selection method for multivariate dynamic models, enhancing forecasting accuracy and interpretability over time.

## Contribution

It develops a novel Bayesian decision-theoretic approach that adaptively selects and weights models based on forecasting goals, differing from traditional methods.

## Key findings

- Improved long-term macroeconomic forecasts.
- Effective identification of relevant models over time.
- Demonstrated advantages over standard model averaging.

## Abstract

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 favored models over time, and is guided by the specific forecasting goals. A synthetic example illustrates how decision-guided variable selection differs from traditional Bayesian model uncertainty analysis and standard model averaging. An applied study in one motivating application of long-term macroeconomic forecasting highlights the utility of the new approach in terms of improving predictions as well as its ability to identify and interpret different sets of relevant models over time with respect to specific, defined forecasting goals.