1999
DOI: 10.1002/(sici)1099-1255(199903/04)14:2<123::aid-jae493>3.0.co;2-k
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A Monte Carlo study of the forecasting performance of empirical SETAR models

Abstract: In this paper we investigate the multi‐period forecast performance of a number of empirical self‐exciting threshold autoregressive (SETAR) models that have been proposed in the literature for modelling exchange rates and GNP, among other variables. We take each of the empirical SETAR models in turn as the DGP to ensure that the ‘non‐linearity’ characterizes the future, and compare the forecast performance of SETAR and linear autoregressive models on a number of quantitative and qualitative criteria. Our result… Show more

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Cited by 112 publications

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“…In fact, out of 36 cells signalling top performance, 16 go to simple benchmarks (AR(1) and random walk), while in 9 additional cases, augmenting the random walk and simple linear prediction models with ARCH-in mean effects, gives accurate predictions. 20 The evidence for French stock returns is mixed, although it is remarkable that in 11 cases we find evidence of accurate forecasting performance from linear predictive models augmented by ARCH-in mean effects.…”
Section: An Overview Of Forecasting Performance
mentioning
confidence: 63%
How this paper cites the one you are viewing
“…In fact, out of 36 cells signalling top performance, 16 go to simple benchmarks (AR(1) and random walk), while in 9 additional cases, augmenting the random walk and simple linear prediction models with ARCH-in mean effects, gives accurate predictions. 20 The evidence for French stock returns is mixed, although it is remarkable that in 11 cases we find evidence of accurate forecasting performance from linear predictive models augmented by ARCH-in mean effects.…”
Section: An Overview Of Forecasting Performance
mentioning
confidence: 63%
How this paper cites the one you are viewing
“…For instance, the article by Diebold and Nason (1990) point out that there is no guarantee that the SETAR model will perform better than the linear AR model. A similar view is expressed in Clements and Smith (1997), where they note that the rejection of a null of linearity in favour of nonlinearity does not guarantee that the prediction based on the SETAR model will outperform the AR models.…”
Section: The Star Models
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confidence: 81%
How this paper cites the one you are viewing
“…The results of the Root Mean Square Forecasting Error (RMSE) also give similar outcome. We therefore find it difficult to substantiate the claim of Boero andMarrocu (2002), andClement andSmith (1997) that the RMSE masks the superiority of a model.…”
Section: Results
mentioning
confidence: 99%