A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models
Christian Kascha ()
No ECO2007/12, Economics Working Papers from European University Institute
Classical Gaussian maximum likelihood estimation of mixed vector autoregressive moving-average models is plagued with various numerical problems and has been considered di±cult by many applied researchers. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. Therefore, several other, simpler estimation methods have been proposed in the literature. In this paper these methods are compared by means of a Monte Carlo study. Different evaluation criteria are used to judge the relative performances of the algorithms.
Keywords: VARMA Models; Estimation Algorithms; Forecasting (search for similar items in EconPapers)
JEL-codes: C32 C15 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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