parcorHijk function

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

This function uses data on two column vectors, xi, xj, and a third set xk, which can be a vector or a matrix. xk usually has the remaining variables in the model, including control variables, if any. This function first removes missing data from all input variables. Then, it computes residuals of OLS (no kernel) regression (xi on xk) and (xj on xk). This hybrid version uses both OLS and then generalized correlation among OLS residuals. This solves the potential problem of having too little information content in kernel regression residuals, since kernel fits are sometimes too close, especially when there are many variables in xk.

parcorHijk(xi, xj, xk)

Arguments

• xi: Input vector of data for variable xi
• xj: Input vector of data for variable xj
• xk: Input data for all 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)
parcorHijk(x[,1], x[,2], x[,3])
## End(Not run)
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See Also

See parcor_ijk.

Author(s)

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