abs_stdapd function

Absolute values of gradients (apd's) of kernel regressions of x on y when both x and y are standardized.

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

abs_stdapd(x, y)

Arguments

• x: vector of data on the dependent variable
• y: data on the regressors which can be a matrix

Returns

Absolute values of kernel regression gradients are returned after standardizing the data on both sides so that the magnitudes of amorphous partial derivatives (apd's) are comparable between regression of x on y on the one hand and regression of y on x on the other.

Details

The first argument is assumed to be the dependent variable. If abs_stdapd(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.

Examples

## Not run:

set.seed(330)
x=sample(20:50)
y=sample(20:50)
abs_stdapd(x,y)
## End(Not run)

Author(s)

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