# absBstdrhserC: Block version abs_stdrhser Absolute residuals kernel... In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

## 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

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

## 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

## 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.

## 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.

## Author(s)

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

## References

Vinod, H. D. 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, https://doi.org/gffn86

See `abs_stdres`.
 ```1 2 3 4 5 6 7 8``` ```## Not run: set.seed(330) x=sample(20:50) y=sample(20:50) z=sample(21:51) absBstdrhserC(x,y,ctrl=z) ## End(Not run) ```