Generalized Method of Moment and Indirect Estimation of the ARASMA Model

Kurt Brännäs () and Xavier de Luna ()
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Xavier de Luna: University College London, Postal: Gower Street, WC1E 6BT London

No 436, Umeå Economic Studies from Umeå University, Department of Economics

Abstract: Estimation in nonlinear time series models has mainly been performed by least squares or maximum likelihood (ML) methods. The paper suggests and studies the performance of generalized method of moments (GMM) and indirect estimators for the autoregressive asymmetric moving average model. Both approaches are easy to implement and perform well numerically. In a Monte Carlo study it is found that the MSE properties of GMM are close to those of ML. The indirect estimator performs poorly in this respect. On the other hand, the three estimation techniques lead to fairly similar power functions for a linearity test.

Keywords: Estimation; Nonlinearity Test; Small Sample Properties; Time Series. (search for similar items in EconPapers)
JEL-codes: C13 C15 C22 (search for similar items in EconPapers)
Pages: 10 pages
Date: 1997-12-15
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Published in Computational Statistics, 1998, pages 485-494.

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:umnees:0436

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