Abstract
Recently, there has been a renewed interest in modeling economic time series by vector autoregressive moving-average models. However, this class of models has been unpopular in practice because of estimation problems and the complexity of the identification stage. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. In this article, several simple estimation methods for vector autoregressive moving-average models are compared among each other and with pure vector autoregressive modeling using ordinary least squares by means of a Monte Carlo study. Different evaluation criteria are used to judge the relative performances of the algorithms.
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