## Abstract Following many applications artificial neural networks (ANNs) have found in hydrology, a question has been rising for quantification of the output uncertainty. A preβoptimized ANN simulated the hydraulic head change at two observation wells, having as input hydrological and meteorologic
A comparison of methods for bootstrapping in the local level model
β Scribed by Glaura C. Franco; Reinaldo C. Souza
- Publisher
- John Wiley and Sons
- Year
- 2001
- Tongue
- English
- Weight
- 106 KB
- Volume
- 21
- Category
- Article
- ISSN
- 0277-6693
- DOI
- 10.1002/for.814
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β¦ Synopsis
Abstract
Bootstrap in time series models is not straightforward to implement, as in this case the observations are not independent. One of the alternatives is to bootstrap the residuals in order to obtain the bootstrap series and thus use these series for inference purposes. This work deals with the problem of assessing the accuracy of hyperparameters in structural models. We study the simplest case, the local level model, where the hyperparameters are given by the variances of the disturbance terms. As their distribution is not known, we employ the bootstrap to approximate the true distribution, using parametric and nonβparametric approaches. Bootstrap standard deviations are computed and their performances compared to the asymptotic and empirical standard errors, calculated using a Monte Carlo simulation. We also build confidence intervals to the hyperparameters, using four bootstrap methods and the results are compared by means of the length, shape and coverage probabilities of the intervals. Copyright Β© 2002 John Wiley & Sons, Ltd.
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