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工具变量可采纳性检验：若干精细渐近结果AI 解读

This paper is concerned with the refined asymptotic properties of several tests for the admissibility of a subset of (overidentifying) instrumental variables. It derives maximum likelihood and linearized maximum likelihood tests and calculates size corrections to the order 1/T. The local power function of the size-corrected tests is the same to the order 1/T, irrespectively of the form of the test statistic or the limited information estimator used in its computation.…

随机展开与渐近近似AI 解读

Under general conditions the distribution function of the first few terms in a stochastic expansion of an econometric estimator or test statistic provides an asymptotic approximation to the distribution function of the original estimator or test statistic with an error of order less than that of the limiting normal or chi-square approximation. This can be used to establish the validity of several refined asymptotic methods, including the comparison of Nagar-type moments and…

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若干工具变量检验程序的改进AI 解读

This paper is concerned with Cornish–Fisher corrections of some instrumental variables test statistics. The tests based on the corrected statistics have size with error of a smaller order of magnitude than the original tests. Symmetric Edgeworth-corrected confidence regions are also defined for the structural parameters. All these corrections are given as analytic formulas that require only limited information, so their implementation is a relatively easy task.

This paper considers the problem of finding the "best" estimator among a class that includes most of the commonly used limited information estimators in simultaneous equation systems. Concentration comparisons, based on the Edgeworth expansion of the distribution of these estimators, lead to selection rules of sufficient simplicity to be useful in applied econometric research.