Asymptotic and bootstrap prediction regions for vector autoregression |
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Authors: | Jae H Kim |
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Institution: | Department of Economics, James Cook University, Townsville, Qld 4811, Australia |
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Abstract: | Small sample properties of asymptotic and bootstrap prediction regions for VAR models are evaluated and compared. Monte Carlo simulations reveal that the bootstrap prediction region based on the percentile-t method outperforms its asymptotic and other bootstrap alternatives in small samples. It provides the most accurate assessment of future uncertainty under both normal and non-normal innovations. The use of an asymptotic prediction region may result in a serious under-estimation of future uncertainty when the sample size is small. When the model is near non-stationary, the use of the bootstrap region based on the percentile-t method is recommended, although extreme care should be taken when it is used for medium to long-term forecasting. |
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Keywords: | VAR model Prediction regions Bootstrap Backward VAR model |
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