1.2.16. statsmodels.api.QuantReg¶
-
class
statsmodels.api.
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.
1.2.16.1. Methods¶
__init__ (endog, exog, **kwargs) |
|
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. |
1.2.16.2. 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 |