Dynamic Asymmetries in U.S. Unemployment
用非线性时间序列模型和贝叶斯方法研究美国失业率的动态非对称性，发现失业率上升比下降更剧烈，对宏观政策制定者和经济研究者有参考价值。
Abstract We examine dynamic asymmetries in U.S. unemployment using nonlinear time series models and Bayesian methods. We find strong statistical evidence in favor of a two-regime threshold auto-regressive model. Empirical results indicate that, once we take into account both parameter and model uncertainty, there are economically interesting asymmetries in the unemployment rate. One finding of particular interest is that shocks that lower the unemployment rate tend to have a smaller effect than shocks that raise the unemployment rate. This finding is consistent with unemployment rises being sudden and falls gradual. KEY WORDS: BayesianNonlinearityThreshold autoregressionUnemployment