# Absolute values of residuals of kernel regressions of x on y when both x and y are standardized.

### Description

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

### Usage

1 | ```
abs_stdres(x, y)
``` |

### Arguments

`x` |
vector of data on the dependent variable |

`y` |
data on the regressors which can be a matrix |

### Details

The first argument is assumed to be the dependent variable. If
`abs_stdres(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, http://dx.doi.org/10.1080/03610918.2015.1122048

### Examples

1 2 3 4 5 6 7 | ```
## Not run:
set.seed(330)
x=sample(20:50)
y=sample(20:50)
abs_stdres(x,y)
## End(Not run)
``` |