In this paper, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with TK explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, a hierarchical mixture model on the TVPs is introduced. The resulting model closely mimics a random coefficients specification which groups the TVPs into several regimes. These flexible mixtures allow for TVPs that feature a small, moderate or large number of structural breaks. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the TK regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our methods to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.

翻译：在本文中,我们将包含K解释变量和T观测的时序参数回归模型(TVP)写成具有传统知识解释变量的恒定系数回归模型。与大部分假设系数随随机行走而演变的现有文献相比,在TVP中引入了等级混合模型。由此形成的模型密切模仿随机系数规格,将TVP分为几种制度。这些灵活混合物允许具有小、中或大数量结构间断特征的TVP。我们根据传统知识递增者的单值分解,开发了高效的Bayesian经济计量方法。在人工数据中,我们发现我们的方法准确,比标准的计算时间方法要快得多。在涉及使用大量预测器进行通货膨胀预测的实验活动中,我们发现我们的方法比其他方法更适合预测,并记录参数变化的不同模式,而不是假设参数随机行走演变的方法。