Sparse Trend Estimation
针对经济变量低频趋势，提出一种基于稀疏先验的稳健贝叶斯估计方法，利用长期调查预期的新事实，在模拟和实证中表现良好，适用于产出增长和年均温度的趋势估计。
Abstract The low-frequency movements of economic variables play a prominent role in policy analysis and decision-making. We develop a robust estimation approach for these slow-moving trend processes which is guided by a judicious choice of priors and is characterized by sparsity. We present novel stylized facts from longer-run survey expectations that inform the structure of the estimation procedure. The general version of the proposed Bayesian estimator with a spike-and-slab prior accounts explicitly for cyclical dynamics. We show that it performs well in simulations against relevant benchmarks and report empirical estimates of trend growth for U.S. output and annual mean temperature.