parcorBijk: Block version of generalized partial correlation coefficients...

In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

Block version of generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.

Description

This function uses data on two column vectors, xi, xj and a third xk which can be a vector or a matrix, usually of the remaining variables in the model, including control variables, if any. It first removes missing data from all input variables. Then, it computes residuals of kernel regression (xi on xk) and (xj on xk). This is a block version of parcor_ijk.

Usage

parcorBijk(xi, xj, xk, blksiz = 10)

Arguments

xi	Input vector of data for variable xi
xj	Input vector of data for variable xj
xk	Input data for variables in xk, usually control variables
blksiz	block size, default=10, if chosen blksiz >n, where n=rows in matrix then blksiz=n. That is, no blocking is done

Value

ouij	Generalized partial correlation Xi with Xj (=cause) after removing xk
ouji	Generalized partial correlation Xj with Xi (=cause) after removing xk

allowing for control variables.

Note

This function calls kern,

Author(s)

Prof. H. D. Vinod, Economics Dept., Fordham University, NY.

See Also

See parcor_ijk.

Examples

## Not run: 
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
options(np.messages=FALSE)
parcorBijk(x[,1], x[,2], x[,3], blksi=10)

## End(Not run)#' 

generalCorr documentation built on Oct. 10, 2023, 1:06 a.m.