 Approximately Exact Inference in Dynamic Panel ModelsSimon Broda,
Marc Paolella and
Yianna Tchopourian
No 368, Computing in Economics and Finance 2006 from Society for Computational Economics Abstract: This paper develops a general method for conducting exact small-sample inference in models which allow the estimator of the (scalar) parameter of interest to be expressed as the root of an estimating function, and which is particularly simple to implement for linear models with a covariance matrix depending on a single parameter. The method involves the computation of tail probabilities of the estimating function. In the context of dynamic panel models, both the least squares and maximum likelihood paradigms give rise to estimating functions involving sums of ratios in quadratic forms in normal variates, the distribution of which cannot be straightforwardly computed. We overcome this obstacle by deriving a saddlepoint approximation that is both readily evaluated and remarkably accurate. A simulation study demonstrates the validity of the procedure, and shows the resulting estimators to be vastly superior over existing ones Keywords: Dynamic Panel Data; Bias Correction; Estimating Equation; Saddlepoint Approximation (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sce:scecfa:368 Access Statistics for this paper More papers in Computing in Economics and Finance 2006 from Society for Computational Economics Contact information at EDIRC. |
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