Var_based Estimation Of The Vector Moving Average Model And Links Between Wholesale And Retail Inventories
We examine a simple estimator for the multivariate moving average model based on vector autoregressive approximation. In finite samples the estimator has a bias which is low where roots of the determinantal equation are well away from the unit circle, and more substantial where one or more roots have modulus near unity. We also examine the sacrifice involved in specifying a vector model for processes which are in fact univariate, and show that the representation estimated by this multivariate technique is asymptotically invertible. This estimator has significant computational advantages over Maximum Likelihood. Moreover, as reported by Galbraith and Zinde-Walsh (1994) for the special case of the univariate model, this estimator can be more robust to mis-specification than ML. The estimation method is applied to a VMA model of wholesale and retail inventories, using Canadian data on overall aggregate, non-durable and durable inventory investment, and allows us to examine the propagation of shocks between the two classes of inventory.
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