abs_stdrhserC function

Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(RHS*y) not gradients.

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.

abs_stdrhserC(x, y, ctrl, ycolumn = 1)

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

Returns

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.

Details

The first argument is assumed to be the dependent variable. If abs_stdrhserC(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)
z=sample(21:51)
abs_stdrhserC(x,y,ctrl=z)
## End(Not run)

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.

Author(s)

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