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The out‐of‐sample forecasts of nonlinear long‐memory models of the real exchange rate
Author(s) -
Chung SangKuck
Publication year - 2006
Publication title -
international journal of finance and economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.505
H-Index - 39
eISSN - 1099-1158
pISSN - 1076-9307
DOI - 10.1002/ijfe.304
Subject(s) - long memory , nonlinear system , econometrics , sample (material) , exchange rate , economics , salient , nonlinear model , series (stratigraphy) , computer science , macroeconomics , artificial intelligence , physics , volatility (finance) , paleontology , quantum mechanics , biology , thermodynamics
We consider a new time series model that can describe long memory and nonlinearity simultaneously and can be used to assess an extensive evaluation of the out‐of‐sample forecasting performance of the nonlinear long‐memory model. Upon fitting it to the real exchange rate, we find that a parsimonious version of the model captures the salient features of the data rather well. We then use this nonlinear long‐memory model to forecast dynamically out‐of‐sample over the sample period for OECD countries. Overall, we find clear evidence that favours the nonlinear long‐memory model over any other estimated models. Copyright © 2006 John Wiley & Sons, Ltd.