Forecasting performance of exponential smooth transition autoregressive exchange rate models

被引:14
作者
Baharumshah, AZ [1 ]
Liew, VKS
机构
[1] Univ Putra Malaysia, Fac Econ & Management, Dept Econ, Serdang 43400, Selangor, Malaysia
[2] Univ Malaysia Sabah, Labuan Sch Int Business & Finance, Kota Kinabalu, Malaysia
关键词
autoregressive; smooth transition autoregressive; nonlinear time series; forecasting accuracy;
D O I
10.1007/s11079-006-6812-7
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper compares the forecasting performance of the Smooth Transition Autoregressive (STAR) model with the conventional linear Autoregressive (AR) and Simple Random Walk (SRW) models. The empirical analysis was conducted using quarterly data for the yen-based currencies of six major East Asian countries. We discovered strong evidence on nonlinear mean reversion in deviation from purchasing power parity (PPP). The results suggest that both the STAR and AR models outperform or at least match the performance of the SRW model. The results also show that the STAR model outperforms the AR model, its linear competitor in a 14-quarter forecast horizon. This finding is consistent with the emerging line of research that emphasizes the importance of allowing nonlinearity in the adjustment of exchange rate.
引用
收藏
页码:235 / 251
页数:17
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