Censored latent effects autoregression, with an application to US unemployment

A new time series model is proposed to describe observed asymmetries in postwar unemployment data. We assume that recession periods, when unemployment increases rapidly, are caused by 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 our censored latent effects autoregression [CLEAR] to monthly US unemployment, where the positive shocks are found to depend on lagged oil prices, industrial production, the term structure of interest rates and a stock market index. The model fits the data well, and its out-of-sample forecasts appear to outperform those from alternative models.