Inference for linear and nonlinear stable error processes via estimating functions |
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Authors: | A Thavaneswaran N Ravishanker Y Liang |
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Institution: | 1. Department of Statistics, University of Manitoba, Canada;2. Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269, USA |
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Abstract: | This paper describes an estimating function approach for parameter estimation in linear and nonlinear times series models with infinite variance stable errors. Joint estimates of location and scale parameters are derived for classes of autoregressive (AR) models and random coefficient autoregressive (RCA) models with stable errors, as well as for AR models with stable autoregressive conditionally heteroscedastic (ARCH) errors. Fast, on-line, recursive parametric estimation for the location parameter based on estimating functions is discussed using simulation studies. A real financial time series is also discussed in some detail. |
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Keywords: | AR model AR-ARCH model RCA model Recursive on-line estimation Sine and cosine estimating functions Stable distribution |
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