Diagnostic Checking in a Flexible Nonlinear Time Series Model
Marcelo Medeiros () and
Alvaro Veiga ()
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Alvaro Veiga: Dept. of Electrical Engineering, Catholic University of Rio de Janeiro, Postal: Rua Marquês de São Vicente 225, Rio de Janeiro, RJ, Brazil 22453-900,
No 386, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics
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 (search for similar items in EconPapers)
JEL-codes: C22 C51 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2000-06-06, Revised 2001-01-15
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Published in Journal of Time Series Analysis, 2003, pages 461-482.
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Journal Article: Diagnostic Checking in a Flexible Nonlinear Time Series Model (2003)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:hastef:0386
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