White's test manually
Posted: Wed Jul 11, 2012 5:35 am
Hello,
I'm trying to construct White's heteroscedasticity test manually in order to be able to apply it to a system of equations. Perhaps there is already someone out there who has done this and if so I'd be very grateful for help with this.
Initially, one would think it shouldn't be that hard: replicate what's done in a VAR object when performing the test to get the same results and then simply use residual series from a system object instead. Sadly, it doesn't seem to be that simple.
White's test without cross-products is, in a univariate case, done in Eviews by regressing the squared residual upon the squared independent variable(s). In the multivariate case, how is the test carried out? The test itself says "(only levels and squares)", which in a bivariate model would mean resid^2 c x^2 y^2, no?
Here's a short example:
The R-squared from the White test in the var model (called "var") is not the same as in the linear regression with res_x as regressand (model called "test"). It's not the same when eliminating the levels from the equation either. For the sake of completeness the program also shows the replication of White's test in the univariate case as mentioned earlier (equation "uni" and "uni_res").
So, the question is: exactly how is White's test without cross-terms carried out in the VAR procedure? If I learned that, then transferring it to a system of equations should not be very hard. Any help appreciated!
I'm trying to construct White's heteroscedasticity test manually in order to be able to apply it to a system of equations. Perhaps there is already someone out there who has done this and if so I'd be very grateful for help with this.
Initially, one would think it shouldn't be that hard: replicate what's done in a VAR object when performing the test to get the same results and then simply use residual series from a system object instead. Sadly, it doesn't seem to be that simple.
White's test without cross-products is, in a univariate case, done in Eviews by regressing the squared residual upon the squared independent variable(s). In the multivariate case, how is the test carried out? The test itself says "(only levels and squares)", which in a bivariate model would mean resid^2 c x^2 y^2, no?
Here's a short example:
Code: Select all
create q 1990q1 1994Q4 'create file and variables
series x = nrnd
series y = nrnd
var var.ls 1 2 x y @ c 'estimate the bivariate var model
show var.white
var.makeresids res_x res_y 'create the residual series
res_x=res_x*res_x 'redefine the residual series as squared residuals
res_y=res_y*res_y
equation test.ls res_x c x^2 y^2 'regress the squared residuals upon levels and squares of the variables
show test
equation uni.ls y c x 'estimate a linear regression model
uni.makeresids resid_uni 'create its residual series
show uni.white
equation uni_res.ls resid_uni^2 c x^2
show uni_resSo, the question is: exactly how is White's test without cross-terms carried out in the VAR procedure? If I learned that, then transferring it to a system of equations should not be very hard. Any help appreciated!