abs_stdrhserr function

Absolute values of Hausman-Wu null in kernel regressions of x on y when both x and y are standardized.

1. standardize the data to force mean zero and variance unity, 2) kernel regress x on y, with the option `gradients = TRUE' and finally 3) compute the absolute values of Hausman-Wu null hypothesis for testing exogeneity, or E(RHS.regressor*error)=0 where error is approximated by kernel regression residuals

abs_stdrhserr(x, y)

Arguments

• x: vector of data on the dependent variable
• y: data on the regressors which can be a matrix

Returns

Absolute values of kernel regression RHS*residuals are returned after standardizing the data on both sides so that the magnitudes of Hausman-Wu null values are comparable between regression of x on y on the one hand and flipped regression of y on x on the other.

Details

The first argument is assumed to be the dependent variable. If abs_stdrhserr(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_stdrhserr(x,y)
## End(Not run)

Author(s)

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