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 between 0 and 1

  • vcov (string, 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 (string, kernel to use in the kernel density estimation for the) –

    asymptotic covariance matrix:

    • epa: Epanechnikov

    • cos: Cosine

    • gau: Gaussian

    • par: Parzene

  • bandwidth (string, 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)