用于广义相关和因果路径的'generalCorr'中R函数的块版本

ERN: Other Macroeconomics: Aggregative Models (Topic) Pub Date : 2019-11-02 DOI:10.2139/ssrn.3478318

H. Vinod

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引用次数: 0

摘要

卡尔·皮尔森在19世纪90年代提出的相关系数r(X,Y)大大低估了两个序列之间的相关性。Vinod(2014)提出了新的广义相关系数，当r*(Y|X) >r*(X|Y)则X是Y的“核心原因”。Vinod(2015)报告了支持核心因果关系的模拟。一个名为“generalCorr”的R软件包(网址:R -project.org)可以计算广义相关性、部分相关性和可能的因果路径。这篇短文描述了2019年10月新添加到“generalCorr”包中的各种R函数的块版本。新出版的《维诺德》(2019)对因果路径背后的理论进行了最新的渲染，包括有证明的定理。建议从业者使用'causeSummBlk(.)'函数。

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Block Versions of R functions in 'generalCorr' for Generalized Correlations and Causal Paths

Karl Pearson developed the correlation coefficient r(X,Y) in 1890s vastly underestimates dependence between two series. Vinod(2014} develops new generalized correlation coefficients so that when r*(Y|X) > r*(X|Y) then X is the "kernel cause'' of Y. Vinod (2015) reports simulations favoring kernel causality. An R software package called 'generalCorr' (at r-project.org) computes generalized correlations, partial correlations, and plausible causal paths. This short paper describes the block versions of various R functions newly added to the 'generalCorr' package in October 2019. Newly published Vinod (2019) has the latest rendering of the theory behind causal paths including theorems with proofs. The function 'causeSummBlk(.)' is recommended for practitioners.

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ERN: Other Macroeconomics: Aggregative Models (Topic)

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