statsmodels.regression.quantile_regression.QuantReg

class statsmodels.regression.quantile_regression.QuantReg(endog, exog, **kwargs)

Quantile Regression

Estimate a quantile regression model using iterative reweighted least squares.

Parameters

endogarray or dataframe

endogenous/response variable

exogarray or dataframe

exogenous/explanatory variable(s)

Notes

The Least Absolute Deviation (LAD) estimator is a special case where quantile is set to 0.5 (q argument of the fit method).

The asymptotic covariance matrix is estimated following the procedure in Greene (2008, p.407-408), using either the logistic or gaussian kernels (kernel argument of the fit method).

References

General:

• Birkes, D. and Y. Dodge(1993). Alternative Methods of Regression, John Wiley and Sons.

• Green,W. H. (2008). Econometric Analysis. Sixth Edition. International Student Edition.

• Koenker, R. (2005). Quantile Regression. New York: Cambridge University Press.

• LeSage, J. P.(1999). Applied Econometrics Using MATLAB,

Kernels (used by the fit method):

• Green (2008) Table 14.2

Bandwidth selection (used by the fit method):

• Bofinger, E. (1975). Estimation of a density function using order statistics. Australian Journal of Statistics 17: 1-17.

• Chamberlain, G. (1994). Quantile regression, censoring, and the structure of wages. In Advances in Econometrics, Vol. 1: Sixth World Congress, ed. C. A. Sims, 171-209. Cambridge: Cambridge University Press.

• Hall, P., and S. Sheather. (1988). On the distribution of the Studentized quantile. Journal of the Royal Statistical Society, Series B 50: 381-391.

Keywords: Least Absolute Deviation(LAD) Regression, Quantile Regression, Regression, Robust Estimation.

Attributes

df_model

The model degree of freedom.

df_resid

The residual degree of freedom.

endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.

Methods

fit([q, vcov, kernel, bandwidth, max_iter, …])	Solve by Iterative Weighted Least Squares
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
get_distribution(params, scale[, exog, …])	Construct a random number generator for the predictive distribution.
hessian(params)	The Hessian matrix of the model.
information(params)	Fisher information matrix of model.
initialize()	Initialize model components.
loglike(params)	Log-likelihood of model.
predict(params[, exog])	Return linear predicted values from a design matrix.
score(params)	Score vector of model.
whiten(data)	QuantReg model whitener does nothing: returns data.

Methods

fit([q, vcov, kernel, bandwidth, max_iter, …])	Solve by Iterative Weighted Least Squares
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
get_distribution(params, scale[, exog, …])	Construct a random number generator for the predictive distribution.
hessian(params)	The Hessian matrix of the model.
information(params)	Fisher information matrix of model.
initialize()	Initialize model components.
loglike(params)	Log-likelihood of model.
predict(params[, exog])	Return linear predicted values from a design matrix.
score(params)	Score vector of model.
whiten(data)	QuantReg model whitener does nothing: returns data.

Properties

df_model	The model degree of freedom.
df_resid	The residual degree of freedom.
endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.