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.

fractional integration, smooth transition autoregression, time series model specification
hdl.handle.net/1765/1660
Econometric Institute Research Papers
Erasmus School of Economics

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