A nonlinear long memory model, with an application to US unemployment
Two important empirical features of US unemployment are that shocks to the series seem rather persistent and that it seems to rise faster during recessions than that it falls during expansions. To jointly capture these features of long memory and nonlinearity, 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.
|Keywords||Fractional integration, Smooth transition autoregression, Time series model specification|
|JEL||Time-Series Models; Dynamic Quantile Regressions (jel C22), Model Construction and Estimation (jel C51), Model Evaluation and Testing (jel C52), Employment; Unemployment; Wages (jel E24), Business Fluctuations; Cycles (jel E32)|
|Persistent URL||dx.doi.org/10.1016/S0304-4076(02)00090-8, hdl.handle.net/1765/11150|
|Journal||Journal of Econometrics|
van Dijk, D.J.C, Franses, Ph.H.B.F, & Paap, R. (2002). A nonlinear long memory model, with an application to US unemployment. In Journal of Econometrics (Vol. 110, pp. 135–165). doi:10.1016/S0304-4076(02)00090-8