We introduce a new time series model that can capture the properties of data as is typically exemplified by monthly US unemployment data. These data show the familiar nonlinear features, with steeper increases in unem- ployment during economic downswings than the decreases during economic prosperity. At the same time, the levels of unemployment in each of the two states do not seem fixed, nor are the transition periods abrupt. Finally, our model should generate out-of-sample forecasts that mimic the in-sample properties. We demonstrate that our new and flexible model covers all those features, and our illustration to monthly US unemployment data shows its merits, both in and out of sample.

Additional Metadata
Keywords Markov switching, duration dependence, Gibbs sampling, unemployment, stochastic levels
JEL Bayesian Analysis (jel C11), Time-Series Models; Dynamic Quantile Regressions (jel C22), Truncated and Censored Models (jel C24), Forecasting and Other Model Applications (jel C53), Employment; Unemployment; Wages (jel E24)
Persistent URL hdl.handle.net/1765/78710
Series Econometric Institute Research Papers
Citation
de Bruijn, L.P, & Franses, Ph.H.B.F. (2015). Stochastic levels and duration dependence in US unemployment (No. EI2015-20). Econometric Institute Research Papers. Retrieved from http://hdl.handle.net/1765/78710