abs_stdapdC: Absolute values of gradients (apd's) of kernel regressions of...

Description Usage Arguments Details Value Author(s) See Also Examples

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 ‘gradients = TRUE’ and finally 3) compute the absolute values of gradients

Usage

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abs_stdapdC(x, y, ctrl)

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

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.

Value

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.

Author(s)

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

See Also

See abs_stdapd.

Examples

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## Not run: 
set.seed(330)
x=sample(20:50)
y=sample(20:50)
z=sample(20:50)
abs_stdapdC(x,y,ctrl=z)

## End(Not run)


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