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.
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Paper 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. Handle: RePEc:hhs:umnees:0436
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