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联立方程模型的两阶段有界影响估计量AI 解读

This article presents a class of estimators for linear structural models that are robust to heavytailed disturbance distributions, gross errors in either the endogenous or exogenous variables, and certain other model failures. The class of estimators modifies ordinary two-stage least squares by replacing each least squares regression by a bounded-influence regression. Conditions under which the estimators are qualitatively robust, consistent, and asymptotically normal are…

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代理变量对回归系数影响的界限AI 解读

We consider a regression in which one of the observed variables is a proxy for some unobserved variable. Given a lower bound for the correlation between the proxy and the unobserved true variable for which it substitutes, we derive intervals in which the coefficients of the unobserved true regression must lie, regardless of any other correlations involving unobserved variables or disturbances. We present a simple solution for the important special case in which only the…

使用加权工具变量的联立方程模型稳健估计AI 解读

IN THIS PAPER we present a weighted-instrumental-variables estimator that is resistant2 to heavy-tailed errors, aberrant data in either the endogenous or exogenous variables, and certain other model failures. The estimator is analogous to the weighted-least-squares approach to robustness proposed by Krasker and Welsch [21] for ordinary regression. We will discuss the theory that motivated this estimator, derive some of its properties, describe our computational algorithm,…

贝叶斯推断中参数模型选择的一则札记AI 解读

This note is concerned with how to replace assessment of a "true" prior on a nonparametric family of distributions--which is usually infeasible--by assessment of an approximating prior with support in a parametrized subfamily, in such a way that the posterior derived from the parametric model is close to the "true" posterior. In general it is not sufficient that the approximating prior be close to the true prior in the sense of weak convergence, and we characterize the…

高效的有界影响回归估计AI 解读

The least squares estimator for β in the classical linear regression model is strongly efficient under certain conditions. However, in the presence of heavy-tailed errors and/or anomalous data, the least squares efficiency can be markedly reduced. In this article we propose an estimator that limits the influence of any small subset of the data and show that it satisfies a first-order condition for strong efficiency subject to the constraint. We then show that the estimator…

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