3.9.1.2.2.1.8. statsmodels.regression.feasible_gls.GLSHet.iterative_fit¶

GLSHet.iterative_fit(maxiter=3)[source]¶

Perform an iterative two-step procedure to estimate a WLS model.

The model is assumed to have heteroscedastic errors. The variance is estimated by OLS regression of the link transformed squared residuals on Z, i.e.:

link(sigma_i) = x_i*gamma.

Parameters:

maxiter : integer, optional

the number of iterations

Notes

maxiter=1: returns the estimated based on given weights maxiter=2: performs a second estimation with the updated weights,

this is 2-step estimation

maxiter>2: iteratively estimate and update the weights

TODO: possible extension stop iteration if change in parameter: estimates is smaller than x_tol

Repeated calls to fit_iterative, will do one redundant pinv_wexog calculation. Calling fit_iterative(maxiter) ones does not do any redundant recalculations (whitening or calculating pinv_wexog).