statsmodels.regression.quantile_regression.QuantReg.fit

QuantReg.fit(q=0.5, vcov='robust', kernel='epa', bandwidth='hsheather', max_iter=1000, p_tol=1e-06, **kwargs)[source]

Solve by Iterative Weighted Least Squares

Parameters:
q : float

Quantile must be strictly between 0 and 1

vcov : str, method used to calculate the variance-covariance matrix

of the parameters. Default is robust:

  • robust : heteroskedasticity robust standard errors (as suggested in Greene 6th edition)

  • iid : iid errors (as in Stata 12)

kernel : str, kernel to use in the kernel density estimation for the

asymptotic covariance matrix:

  • epa: Epanechnikov

  • cos: Cosine

  • gau: Gaussian

  • par: Parzene

bandwidth : str, Bandwidth selection method in kernel density

estimation for asymptotic covariance estimate (full references in QuantReg docstring):

  • hsheather: Hall-Sheather (1988)

  • bofinger: Bofinger (1975)

  • chamberlain: Chamberlain (1994)