A suboptimal bootstrap method for structure detection of non-linear output-error models with application to human ankle dynamics

2005 ◽  
Vol 78 (12) ◽  
pp. 937-948 ◽  
Author(s):  
Sunil L. Kukreja *
Keyword(s):  
Bootstrap Method ◽  
Error Models ◽  
Output Error ◽  
Structure Detection ◽  
Human Ankle ◽  
Non Linear

Piecewise-Linear Output-Error Models

2003 ◽  
Vol 36 (16) ◽  
pp. 1795-1800 ◽  
Author(s):  
Fredrik Rosenqvist ◽  
Anders Karlström
Keyword(s):  
Piecewise Linear ◽  
Error Models ◽  
Output Error

Input Design for Continuous Time Output Error Models

2019 ◽  
Vol 58 (26) ◽  
pp. 11175-11186
Author(s):  
Santhosh Kumar Varanasi ◽  
Chaitanya Manchikatla ◽  
Phanindra Jampana
Keyword(s):  
Continuous Time ◽  
Error Models ◽  
Output Error ◽  
Input Design

SPECIFICATION TESTS FOR MULTIPLICATIVE ERROR MODELS

2016 ◽  
Vol 33 (2) ◽  
pp. 413-438 ◽  
Author(s):  
Indeewara Perera ◽  
Mervyn J. Silvapulle
Keyword(s):  
Trading Volume ◽  
Bootstrap Method ◽  
Realized Volatility ◽  
Error Distribution ◽  
Parametric Models ◽  
Error Models ◽  
Conditional Mean ◽  
Multiplicative Error ◽  
Financial Transactions ◽  
Multiplicative Error Models

The family of multiplicative error models is important for studying non-negative variables such as realized volatility, trading volume, and duration between consecutive financial transactions. Methods are developed for testing the parametric specification of a multiplicative error model, which consists of separate parametric models for the conditional mean and the error distribution. The same method can also be used for testing the specification of the error distribution provided the conditional mean is correctly specified. A bootstrap method is proposed for computing the p-values of the tests and is shown to be consistent. The proposed tests have nontrivial asymptotic power against a class of O(n−1/2)-local alternatives. The tests performed well in a simulation study, and they are illustrated using a data example on realized volatility.

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