Generalized Method of Moment and Indirect Estimation of the ARASMA Model
AbstractEstimation 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.
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Bibliographic InfoPaper provided by Umeå University, Department of Economics in its series Umeå Economic Studies with number 436.
Length: 10 pages
Date of creation: 15 Dec 1997
Date of revision:
Publication status: Published in Computational Statistics, 1998, pages 485-494.
Contact details of provider:
Postal: Department of Economics, Umeå University, S-901 87 Umeå, Sweden
Phone: 090 - 786 61 42
Fax: 090 - 77 23 02
Web page: http://www.econ.umu.se/
More information through EDIRC
Estimation; Nonlinearity Test; Small Sample Properties; Time Series.;
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