stdres {generalCorr}

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 residuals. The standardization yields comparable residuals.

Usage

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 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

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 the flipped 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, doi:10.1080/03610918.2015.1122048

Examples

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

## End(Not run)