Numerical methods for the recursive estimation of large-scale linear econometric models
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Recursive estimation is an essential procedure in econometrics which appears in many applications when the underlying dataset or model is modi ed. Data arrive consecutively and thus already estimated models will have to be updated with new available information. Moreover, in many cases, data will have to be deleted from a model in order to remove their effect, either because they are old (obsolete) or because they have been detected to be outliers or extreme values and further investigation is required. The aim of this thesis is to develop numerically stable and computationally efficient methods for the recursive estimation of large-scale linear econometric models. Estimation of multivariate linear models is a computationally costly procedure even for moderate-sized models. In particular, when the model needs to be estimated recursively, its estimation will be even more computationally demanding. Moreover, conventional methods yield often, misleading results. The aim is to derive new methods which effectively utilise previous computations, in order to reduce the high computational cost, and which provide accurate results as well. Novel numerical methods for the recursive estimation of the general linear, the seemingly unrelated regressions, the simultaneous equations, the univariate and multivariate timevarying parameters models are developed. The proposed methods are based on numerically stable strategies which provide accurate and precise results. Moreover, the new methods estimate the unknown parameters of the modi ed model even when the variance covariance matrix is singular.
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