scholarly journals Bootstrap Power of Time Series Goodness of fit tests

2013 ◽  
Vol 9 (2) ◽  
pp. 155
Author(s):  
Sohail Chand ◽  
Shahid Kamal
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Goodness Of Fit Tests

Score based goodness-of-fit tests for time series

2011 ◽  
Vol 21 (4) ◽  
Author(s):  
Shiqing Ling ◽  
Howell Tong
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Goodness Of Fit Tests

Goodness‐of‐fit tests for β ARMA hydrological time series modeling

2020 ◽  
Vol 31 (3) ◽  
Author(s):  
Vinícius T. Scher ◽  
Francisco Cribari‐Neto ◽  
Guilherme Pumi ◽  
Fábio M. Bayer
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Time Series Modeling ◽  
Goodness Of Fit Tests

scholarly journals Dependent SiZer: Goodness-of-Fit Tests for Time Series Models

2004 ◽  
Vol 31 (8) ◽  
pp. 999-1017 ◽  
Author(s):  
Cheolwoo Park ◽  
J. S. Marron ◽  
Vitaliana Rondonotti
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Time Series Models ◽  
Goodness Of Fit Tests

scholarly journals GOODNESS-OF-FIT TESTS FOR MULTIVARIATE COPULA-BASED TIME SERIES MODELS

2016 ◽  
Vol 33 (2) ◽  
pp. 292-330 ◽  
Author(s):  
Betina Berghaus ◽  
Axel Bücher
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Mixing Time ◽  
Parametric Bootstrap ◽  
Stationary Time Series ◽  
Time Series Models ◽  
Finite Sample ◽  
Copula Functions ◽  
Cross Sectional ◽  
Goodness Of Fit Tests

In recent years, stationary time series models based on copula functions became increasingly popular in econometrics to model nonlinear temporal and cross-sectional dependencies. Within these models, we consider the problem of testing the goodness-of-fit of the parametric form of the underlying copula. Our approach is based on a dependent multiplier bootstrap and it can be applied to any stationary, strongly mixing time series. The method extends recent i.i.d. results by Kojadinovic et al. (2011) and shares the same computational benefits compared to methods based on a parametric bootstrap. The finite-sample performance of our approach is investigated by Monte Carlo experiments for the case of copula-based Markovian time series models.

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