parcor_ijk function

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

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). The function reports the generalized correlation between two kernel residuals. This version avoids ridge type adjustment present in an older version.

parcor_ijk(xi, xj, xk)

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

Returns

• 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,

Examples

## Not run:

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

See Also

See parcor_linear.

Author(s)

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