.png){kind=icon}
Return
Bootstrap specification tests for dynamic conditional distribution models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jeconom.2022.08.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes bootstrap based tests for the specification of a given parametric conditional distribution in autoregressive time series with GARCH-type disturbances. The tests are based on an estimated residual empirical process and are implemented by parametric bootstrap. We show that the proposed tests are asymptotically valid, consistent, and have nontrivial asymptotic power against a large proportion of local alternatives. Our approach relies on non-primitive regularity conditions and certain properties of exponential almost sure convergence. The regularity conditions are shown to be satisfied by GARCH(p,q); this technique of verification is applicable to other models as well. In our Monte Carlo study, the proposed tests performed well and better than several competing tests, including the information matrix test. A real data example illustrates the testing procedure.(C) 2022 Elsevier B.V. All rights reserved.
Keywords:
GARCH
Goodness-of-fit
Residual empirical process
Kolmogorov-Smirnov test
Lack-of-fit test
Stochastic recurrence equations
Journal
IF:
4
Papers:
5.2K
Citations:
3.0W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more