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2011
Springer

Estimating structural VARMA models with uncorrelated but non-independent error terms

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Estimating structural VARMA models with uncorrelated but non-independent error terms
The asymptotic properties of the quasi-maximum likelihood estimator (QMLE) of vector autoregressive moving-average (VARMA) models are derived under the assumption that the errors are uncorrelated but not necessarily independent. Relaxing the independence assumption considerably extends the range of application of the VARMA models, and allows to cover linear representations of general nonlinear processes. Conditions are given for the consistency and asymptotic normality of the QMLE. A particular attention is given to the estimation of the asymptotic variance matrix, which may be very different from that obtained in the standard framework. Modified versions of the Wald, Lagrange Multiplier and Likelihood Ratio tests are proposed for testing linear restrictions on the parameters. Key words: Echelon form, Lagrange Multiplier test, Likelihood Ratio test, Nonlinear processes, QMLE, Structural representation, VARMA models, Wald test.
Y. Boubacar Mainassara, Christian Francq
Added 14 May 2011
Updated 14 May 2011
Type Journal
Year 2011
Where MA
Authors Y. Boubacar Mainassara, Christian Francq
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