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:
qfloat

Quantile must be strictly between 0 and 1.

vcovstr

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)

kernelstr

Kernel to use in the kernel density estimation for the asymptotic covariance matrix:

  • epa: Epanechnikov

  • cos: Cosine

  • gau: Gaussian

  • par: Parzen

bandwidthstr

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)

max_iterint

Maximum number of iterations.

p_tolfloat

Convergence tolerance for the iterative parameter estimates.

**kwargs

Additional keyword arguments, accepted for API compatibility.

Returns:
RegressionResultsWrapper

Results instance for the fitted quantile regression, with additional q, iterations, sparsity, bandwidth, and history attributes.