abs_stdrhserr {generalCorr} | R Documentation |
Absolute values of Hausman-Wu null in 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 ‘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
Usage
abs_stdrhserr(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_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.
Value
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.
Author(s)
Prof. H. D. Vinod, Economics Dept., Fordham University, NY
Examples
## Not run:
set.seed(330)
x=sample(20:50)
y=sample(20:50)
abs_stdrhserr(x,y)
## End(Not run)