SSE/EFI Working Paper Series in Economics and Finance
No 386:
Diagnostic Checking in a Flexible Nonlinear Time Series Model
Marcelo Medeiros ()
and Alvaro Veiga ()
Abstract: This paper considers a sequence of misspecification tests
for a flexible nonlinear time series model. The model is a generalization
of both the Smooth Transition AutoRegressive (STAR) and the AutoRegressive
Artificial Artificial Neural Network (AR-ANN) models. The tests are
Lagrange multiplier (LM) type tests of parameter constancy against the
alternative of smoothly changing ones, of serial independence, and constant
variance of the error term against the hypothesis that the variance
smoothly changes between regimes. The small sample behaviour of the
proposed tests is evaluated throw a Monte-Carlo study and the results show
that the tests have size close to the nominal one and a good power.
Keywords: Time series; nonlinear models; STAR models; neural networks; statistical inference; parameter constancy; serial independence; heteroscedasticity; misspecification; (follow links to similar papers)
JEL-Codes: C22; C51; (follow links to similar papers)
24 pages, June 6, 2000, Revised January 15, 2001
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- This paper is published as:
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Medeiros, Marcelo and Alvaro Veiga, (2003), 'Diagnostic Checking in a Flexible Nonlinear Time Series Model', Journal of Time Series Analysis, Vol. 24, July, No. 4, pages 461-482
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