k 类估计器和 2SGMM 估计器的 Frisch-Waugh-Lovell 定理类型结果

IF 0.9 4区 数学 Q3 STATISTICS & PROBABILITY
Deepankar Basu
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引用次数: 0

摘要

Frisch-Waugh-Lovell (FWL) 定理表明,对于最小二乘估计器,完全模型和部分模型的参数估计值完全相同。我的研究表明,在混合了外生和内生回归因子的线性回归模型中,k 类估计器(包括 LIML)和两步最优 GMM 估计器的 FWL 定理类型结果成立。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frisch–Waugh–Lovell theorem-type results for the k-Class and 2SGMM estimators

The Frisch–Waugh–Lovell (FWL) theorem shows that for the least squares estimator, parameter estimates from full and partial models are identically same. I show that in linear regression models with a mix of exogenous and endogenous regressors, FWL theorem-type results hold for the k-class estimators (including LIML) and the two-step optimal GMM estimator.

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来源期刊
Statistics & Probability Letters
Statistics & Probability Letters 数学-统计学与概率论
CiteScore
1.60
自引率
0.00%
发文量
173
审稿时长
6 months
期刊介绍: Statistics & Probability Letters adopts a novel and highly innovative approach to the publication of research findings in statistics and probability. It features concise articles, rapid publication and broad coverage of the statistics and probability literature. Statistics & Probability Letters is a refereed journal. Articles will be limited to six journal pages (13 double-space typed pages) including references and figures. Apart from the six-page limitation, originality, quality and clarity will be the criteria for choosing the material to be published in Statistics & Probability Letters. Every attempt will be made to provide the first review of a submitted manuscript within three months of submission. The proliferation of literature and long publication delays have made it difficult for researchers and practitioners to keep up with new developments outside of, or even within, their specialization. The aim of Statistics & Probability Letters is to help to alleviate this problem. Concise communications (letters) allow readers to quickly and easily digest large amounts of material and to stay up-to-date with developments in all areas of statistics and probability. The mainstream of Letters will focus on new statistical methods, theoretical results, and innovative applications of statistics and probability to other scientific disciplines. Key results and central ideas must be presented in a clear and concise manner. These results may be part of a larger study that the author will submit at a later time as a full length paper to SPL or to another journal. Theory and methodology may be published with proofs omitted, or only sketched, but only if sufficient support material is provided so that the findings can be verified. Empirical and computational results that are of significant value will be published.
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