摘要翻译：
时变参数(TVP)模型具有过参数化的可能性，特别是当模型中的变量数目较大时。在这类模型中，全局-局部先验越来越多地被用来诱导收缩。但这些先验所产生的估计仍有相当大的不确定性。稀疏化有可能减少这种不确定性并改善预测。在本文中，我们发展了计算简单的方法，收缩和稀疏TVP模型。在一个模拟数据练习中，我们展示了我们的收缩-然后-稀疏方法在各种稀疏和稠密TVP回归中的好处。在宏观经济预测练习中，我们发现我们的方法可以大大提高相对于萎缩的预测性能。
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英文标题：
《Inducing Sparsity and Shrinkage in Time-Varying Parameter Models》
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作者：
Florian Huber, Gary Koop, Luca Onorante
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  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 reduce 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 forecasting exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.
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PDF链接：
https://arxiv.org/pdf/1905.10787