absBstdres function

Block version of abs-stdres Absolute values of residuals of kernel regressions of standardized x on standardized y, no control variables.

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.

absBstdres(x, y, blksiz = 10)

Arguments

• x: vector of data on the dependent variable
• y: data on the regressors which can be a matrix
• blksiz: block size, default=10, if chosen blksiz >n, where n=rows in matrix then blksiz=n. That is, no blocking is done

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_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.

Examples

## Not run:

set.seed(330)
x=sample(20:50)
y=sample(20:50)
abs_stdres(x,y)
## 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")

Author(s)

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