statsmodels.regression.quantile_regression.QuantReg
-
class statsmodels.regression.quantile_regression.QuantReg(endog, exog, **kwargs)
[source] -
Quantile Regression
Estimate a quantile regression model using iterative reweighted least squares.
Parameters: endog : array or dataframe
endogenous/response variable
exog : array 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.
Methods
fit
([q, vcov, kernel, bandwidth, max_iter, ...])Solve by Iterative Weighted Least Squares fit_regularized
([method, maxiter, alpha, ...])Return a regularized fit to a linear regression model. from_formula
(formula, data[, subset])Create a Model from a formula and dataframe. hessian
(params)The Hessian matrix of the model information
(params)Fisher information matrix of model initialize
()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. Attributes
df_model
The model degree of freedom, defined as the rank of the regressor matrix minus 1 if a constant is included. df_resid
The residual degree of freedom, defined as the number of observations minus the rank of the regressor matrix. endog_names
exog_names
Please login to continue.