A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output
通过贝叶斯模型比较多种常用的产出趋势-周期分解方法，发现包含趋势与周期创新相关性和2007年趋势增长断点的不可观测成分模型最优，且趋势增长年率从约3.4%降至1.2%-1.5%。
We compare a number of widely used trend‐cycle decompositions of output in a formal Bayesian model comparison exercise. This is motivated by the often markedly different results from these decompositions—different decompositions have broad implications for the relative importance of real versus nominal shocks in explaining variations in output. Using U.S. quarterly real GDP, we find that the overall best model is an unobserved components model with two features: (i) a nonzero correlation between trend and cycle innovations and (ii) a break in trend output growth in 2007. The annualized trend output growth decreases from about 3.4% to 1.2%–1.5% after the break. The results also indicate that real shocks are more important than nominal shocks. The slowdown in trend output growth is robust when we expand the set of models to include bivariate unobserved components models.