Two important empirical features of monthly US unemployment are that shocks to the series seem rather persistent and that unemployment seems to rise faster in recessions than that it falls during expansions. To jointly capture these features of long memory and nonlinearity, respectively, we put forward a new time series model and evaluate its empirical performance. We find that the model describes the data rather well and that it outperforms related competitive models on various measures of fit.

Additional Metadata
Keywords fractional integration, smooth transition autoregression, time series model specification
Persistent URL hdl.handle.net/1765/1660
Series Econometric Institute Research Papers
Citation
van Dijk, D.J.C, Franses, Ph.H.B.F, & Paap, R. (2000). A nonlinear long memory model for US unemployment (No. EI 2000-30/A). Econometric Institute Research Papers. Retrieved from http://hdl.handle.net/1765/1660