Inducing Sparsity and Shrinkage in Time-Varying Parameter Models
针对时变参数模型容易过度参数化的问题，本文提出一种计算简单的方法，同时实现收缩和稀疏化，在模拟和宏观经济预测中相比仅用收缩方法显著提升了预测表现。
Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to remove this uncertainty and improve forecasts. In this paper, we develop computationally simple methods which both shrink and sparsify TVP models. In a simulated data exercise we show the benefits of our shrink-then-sparsify approach in a variety of sparse and dense TVP regressions. In a macroeconomic forecast exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.