We discuss how econometric estimators may be efficiently programmed in Mata. The prevalence of matrix-based analytical derivations of estimation techniques and the computational improvements available from just-in-time compilation combine to make Mata the tool of choice for econometric implementation. Two examples are given: computing the seemingly unrelated regression (SUR) estimator for an unbalanced panel, a multivariate linear approach, and computing the continuously updated GMM estimator (GMM-CUE) for a linear instrumental variables model. The GMM-CUE estimator makes use of Mata's optimize suite of functions. Both illustrate the power and effectiveness of a Mata-based approach.
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