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Asymptotic Fisher information matrix of Markov switching VARMA models

Maddalena Cavicchioli

Journal of Multivariate Analysis, 2017, vol. 157, issue C, 124-135

Abstract: We study the Fisher information (FI) matrix of Markov switching vector autoregressive moving average (MS VARMA) models and derive an explicit expression in closed form for the asymptotic FI matrix of the underlying model. Our result is more general than the available one in the literature for linear VARMA models, which has been recently studied in Bao and Hua (2014), in two respects. First, we treat the variance of the error term in a more general setting rather than considering it as a nuisance parameter. Then, we consider non-trivial intercept in the MS VARMA model. Under general conditions, the asymptotic FI matrix can be used to derive the asymptotic covariance matrix of the Gaussian maximum likelihood estimator of the model parameters. Some examples and numerical applications illustrate the results.

Keywords: Time series with changes in regime; Information matrix computation; Markov switching VARMA; Asymptotic covariance matrix; Gaussian maximum likelihood estimator (search for similar items in EconPapers)
Date: 2017
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