Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105231
Title: Multi-step prediction for nonlinear autoregressive models based on empirical distributions
Authors: Guo, M.
Bai, Z. 
An, H.Z.
Keywords: Empirical distribution
Multi-step prediction
Nonlinear autoregressive model
Issue Date: Apr-1999
Citation: Guo, M.,Bai, Z.,An, H.Z. (1999-04). Multi-step prediction for nonlinear autoregressive models based on empirical distributions. Statistica Sinica 9 (2) : 559-570. ScholarBank@NUS Repository.
Abstract: A multi-step prediction procedure for nonlinear autoregressive (NLAR) models based on empirical distributions is proposed. Calculations involved in this prediction scheme are rather simple. It is shown that the proposed predictors are asymptotically equivalent to the exact least squares multi-step predictors, which are computable only when the innovation distribution has a simple known form. Simulation studies are conducted for two- and three-step predictors of two NLAR models.
Source Title: Statistica Sinica
URI: http://scholarbank.nus.edu.sg/handle/10635/105231
ISSN: 10170405
Appears in Collections:Staff Publications

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