generalCorr (version 1.2.6)

absBstdrhserC: Block version abs_stdrhser Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(Resid*RHS).

Description

1) standardize the data to force mean zero and variance unity, 2) kernel regress x on y and a matrix of control variables, with the option `residuals = TRUE' and finally 3) compute the absolute values of residuals.

Usage

absBstdrhserC(x, y, ctrl, ycolumn = 1, blksiz = 10)

Value

Absolute values of kernel regression residuals are returned after standardizing the data on both sides so that the magnitudes of residuals are comparable between regression of x on y on the one hand and regression of y on x on the other.

Arguments

x

vector of data on the dependent variable

y

data on the regressors which can be a matrix

ctrl

Data matrix on the control variable(s) beyond causal path issues

ycolumn

if y has more than one column, the column number used when multiplying residuals times this column of y, default=1 or first column of y matrix is used

blksiz

block size, default=10, if chosen blksiz >n, where n=rows in matrix then blksiz=n. That is, no blocking is done

Author

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

Details

The first argument is assumed to be the dependent variable. If absBstdrhserC(x,y) is used, you are regressing x on y (not the usual y on x). The regressors can be a matrix with 2 or more columns. The missing values are suitably ignored by the standardization.

References

Vinod, H. D. 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")

See Also

See abs_stdres.

Examples

if (FALSE) {
set.seed(330)
x=sample(20:50)
y=sample(20:50)
z=sample(21:51)
absBstdrhserC(x,y,ctrl=z)
}