Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models
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 c...
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unimas-185952017-11-27T06:33:33Z http://ir.unimas.my/18595/ Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models Ahmad Zubaidi, Baharumshah Liew, Venus Khim-Sen HB Economic Theory 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. Springer Science + Business Media, Inc. 2006 Article PeerReviewed text en http://ir.unimas.my/18595/7/Forecasting%20Performance%20of%20Exponential%20%28abstract%29.pdf Ahmad Zubaidi, Baharumshah and Liew, Venus Khim-Sen (2006) Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models. Open economies review, 17 (2). pp. 235-251. ISSN 0923-7992 https://link.springer.com/article/10.1007/s11079-006-6812-7 |
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Universiti Malaysia Sarawak |
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UNIMAS Institutional Repository |
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Online Access |
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English |
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HB Economic Theory |
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HB Economic Theory Ahmad Zubaidi, Baharumshah Liew, Venus Khim-Sen Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models |
description |
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. |
format |
Article |
author |
Ahmad Zubaidi, Baharumshah Liew, Venus Khim-Sen |
author_facet |
Ahmad Zubaidi, Baharumshah Liew, Venus Khim-Sen |
author_sort |
Ahmad Zubaidi, Baharumshah |
title |
Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models |
title_short |
Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models |
title_full |
Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models |
title_fullStr |
Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models |
title_full_unstemmed |
Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models |
title_sort |
forecasting performance of exponential smooth transition autoregressive exchange rate models |
publisher |
Springer Science + Business Media, Inc. |
publishDate |
2006 |
url |
http://ir.unimas.my/18595/ http://ir.unimas.my/18595/ http://ir.unimas.my/18595/7/Forecasting%20Performance%20of%20Exponential%20%28abstract%29.pdf |
first_indexed |
2018-09-06T16:44:41Z |
last_indexed |
2018-09-06T16:44:41Z |
_version_ |
1610877272339775488 |