Document Type
Journal Article
Department/Unit
Department of Mathematics
Title
Heteroscedasticity and/or autocorrelation diagnostics in nonlinear models with AR(1) and symmetrical errors
Language
English
Abstract
In this paper, we discuss tests of heteroscedasticity and/or autocorrelation in nonlinear models with AR(1) and symmetrical errors. The symmetrical errors distribution class includes all symmetrical continuous distributions, such as normal, Student-t, power exponential, logistic I and II, contaminated normal, so on. First, score test statistics and their adjustment forms of heteroscedasticity are derived. Then, the asymptotic properties, including asymptotic chi-square and approximate powers under local alternatives of the score tests, are studied. The properties of test statistics are investigated through Monte Carlo simulations. Finally, a real data set is used to illustrate our test methods. © 2008 Springer-Verlag.
Keywords
Approximate local powers, AR(1) errors, Asymptotic properties, Heteroscedasticity, Nonlinear model, Score test, Symmetrical distributions
Publication Date
2010
Source Publication Title
Statistical Papers
Volume
51
Issue
4
Start Page
813
End Page
836
Publisher
Springer-Verlag
DOI
10.1007/s00362-008-0171-y
Link to Publisher's Edition
ISSN (print)
09325026
ISSN (electronic)
16139798
Recommended Citation
Cao, Chun-Zheng, Jin-Guan Lin, and Li-Xing Zhu. "Heteroscedasticity and/or autocorrelation diagnostics in nonlinear models with AR(1) and symmetrical errors." Statistical Papers 51.4 (2010): 813-836.