Nonlinear Adjustment and Generalized Impulse Response Function in Real Exchange Rates

In this paper we considered new time series model which can describe long memory and nonlinearity simultaneously, and which can be used to assess the relative importance of these attributes in empirical financial time series. Upon fitting it to the monthly deviations from PPP for six countries, we found that a parsimonious version of the model captures the salient features of the data rather well. When we compared the model with various competitive models, we found that a linear fractionally integrated model could certainly be improved upon by including nonlinear features. Indeed, once we added these, there remained no evidence of nonlinearity and time-varying parameters. However, the introduction of long memory into a STAR model did not lead to an improved fit. The key distinction between these two models lies in their implied long-run properties. We highlighted these differences using impulse response functions and related measures of the persistence of shocks.