Modified Gaussian likelihood estimators for ARMA models on

Chrysoula Dimitriou-Fakalou

Stochastic Processes and their Applications, 2009, vol. 119, issue 12, 4149-4175

Abstract: For observations from an auto-regressive moving-average process on any number of dimensions, we propose a modification of the Gaussian likelihood, which when maximized corrects the edge-effects and fixes the order of the bias for the estimators derived. We show that the new estimators are not only consistent but also asymptotically normal for any dimensionality. A classical one-dimensional, time series result for the variance matrix is established on any number of dimensions and guarantees the efficiency of the estimators, if the original process is Gaussian. We have followed a model-based approach and we have used finite numbers for the corrections per dimension, which are especially made for the case of the auto-regressive moving-average models of fixed order.

Keywords: Auto-regressive; moving-average; model; Edge-effect; Maximum; likelihood; estimation; Second-order; properties (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (3)

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