A0961
Title: Simultaneous graphical dynamic linear models for macroeconomic policy
Authors: Meng Xie - Duke University (United States) [presenting]
Knut Are Aastveit - Norges Bank (Norway)
Kaoru Irie - University of Tokyo (Japan)
Mike West - Duke University (United States)
Abstract: There is abundant interest in incorporating information from many economic time series to improve forecasts and inform policy. However, traditional methods, including time-varying vector autoregression models, can become over-parameterized as the number of series increases. Simultaneous graphical dynamic linear models (SGDLMs) provide a flexible and computationally efficient approach for modeling macroeconomic series, and also enable novel order-free structural analyses for policy decision-making. In SGDLMs, each data series is modeled with its own specialized and limited set of sparse predictors, with simultaneous relationships represented through contemporaneous predictors. At each time point, the posterior distributions of states and volatilities in each of the univariate models are independently updated for the new observation. Then, the series-specific models are recoupled to account for cross-series dependencies, and decoupled again to continue sequential estimation and forecasting. We apply SGDLMs to forecast macroeconomic series from the Federal Reserve Economic Data database, using interventions to sequential updating for dynamic variable selection.
