BKW multicollinearity diagnostic
[condindex]=bkw(arg1,...,argn)
* argi = arguments which can be:
- a time series
- a real (nxp) vector
- a string equal to the name of a time series or a (nxp) real vector between quotes
- the string 'noprint' if the user doesn't want to print the results of the regression
* condindex = the condition number
x = grand(200,5,'nor',0,1); x(:,1) = ones(200,1); x(:,3) = x(:,2) + grand(200,1,'nor',0,1)*0.05; // create multioncolinearity between columns 2 and 3 bkw('x') // This example is taken from bkw_d. Here the exogenous variables take the form of a matrix x; // it is entered between quotes, so the exogenous variables will be called x_1, x_2,... | ![]() | ![]() |