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Forecasting Performance of Logistic STAR Exchange Rate Model: The Original and Reparameterised Versions

Venus Liew () and Ahmad Zubaidi Baharumshah ()

GE, Growth, Math methods from University Library of Munich, Germany

Abstract: Exponential Smooth Transition Autoregressive (ESTAR) model is widely adopted in the exchange rate study as its symmetrical distribution matches that of the symmetrical exchange rate adjustment behaviour. In contrast, another specification of STAR model, namely the LSTAR (logistic STAR) model is discarded by most researchers in priori in their exchange rate modeling exercises due to its undesired property of being asymmetry. This study is the first of its kind in examining the validity of this hypothesis that the ESTAR exchange rate model is superior to LSTAR exchange rate model on the basis of forecasting accuracy. Based on the experience of the adjustment process of two nominal exchange rates, we find that the hypothesis is merely theoretical since we fail to provide consistent empirical evidence in favour of the null hypothesis. This warrants us that we need not be too pessimistic on the usage of LSTAR model in exchange rate study. In our effort to rekindle the usage of LSTAR model, we further reparameterized the original version into the so-called absolute version, which has symmetrical distribution properties, in accordance with the well-known symmetrical adjustment process of exchange rate. The resulting ALSTAR model has proven to be a more promising model in the sense that it has improved significantly from its original version as well as the ESTAR model, which has thus far been deemed the most appropriate nonlinear exchange rate model.

Keywords: LSTAR; ESTAR; forecasting accuracy; nonlinear; exchange rate (search for similar items in EconPapers)
JEL-codes: C53 F31 (search for similar items in EconPapers)
Date: 2003-08-03
Note: Type of Document -
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Working Paper: Forecasting performance of Logistic STAR exchange rate model: The original and reparameterised versions (2002) Downloads
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