Censored latent effects autoregression, with an application to US unemployment
提出一种删失潜在效应自回归模型，用于描述美国战后失业率时间序列中的非对称性，发现衰退期失业率快速上升与未观测到的正向冲击有关，且这些冲击可通过领先指标预测，模型拟合和预测效果优于其他模型。
Abstract A model is proposed to describe observed asymmetries in postwar unemployment time series data. We assume that recession periods, when unemployment increases rapidly, correspond with unobserved positive shocks. The generating mechanism of these latent shocks is a censored regression model, where linear combinations of lagged explanatory variables lead to positive shocks, while otherwise shocks are equal to zero. We apply this censored latent effects autoregression to monthly US unemployment, where the positive shocks are found to be predictable using various leading indicators. The model fits the data well and its out‐of‐sample forecasts appear to improve on those from alternative models. Copyright © 2002 John Wiley & Sons, Ltd.