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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)

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