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基于似然的渐近预测函数AI 解读

This paper develops asymptotic prediction functions that approximate the shape of the density of future observations and correct for parameter uncertainty. The functions are based on extensions to a definition of predictive likelihood originally suggested by S. L. Lauritzen (1974) and D. Hinkley (1979). The prediction function is shown to possess efficiency properties based on the Kullback-Leibler measure of information loss. Examples of the application of the prediction…

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宏观计量模型比较与评价技术：实践评估AI 解读

Abstract This paper examines four model comparison techniques in the setting of an actual large‐scale U.S. macroeconomic model. The techniques include formal hypothesis tests, mean squared error analysis, and two new tests based on the complementary conditions that the structural and reduced form disturbances should be orthogonal to the predetermined variables. The latter two tests can be conveniently implemented by regressing either structural residuals or reduced form…

伪极大似然估计：渐近分布AI 解读

Gong and Samaniego (1981) define pseudo maximum likelihood estimation and derive the asymptotic distribution of the resulting estimates. This note gives a simpler and more elegant expression for the asymptotic variance of a pseudo maximum likelihood estimate.

大型非线性模型的 FIML 与 3SLS 估计算法AI 解读

THIS PAPER PRESENTS a numerical algorithm for computing full information maximum likelihood (FIML) and nonlinear three-stage least squares (3SLS) coefficient estimates for large nonlinear models. (The proposed algorithm will be denoted algorithm A.) Although the theory of FIML estimation has been available for thirty years [20], FIML estimation has long been regarded as impossible on large nonlinear models.2 The more recently proposed nonlinear 3SLS estimator [191 also poses…