Quantile Regression
Estimate a quantile regression model using iterative reweighted least squares.
Parameters: | endog : array or dataframe
exog : array or dataframe
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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:
Kernels (used by the fit method):
Bandwidth selection (used by the fit method):
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 |
from_formula(formula, data[, subset, drop_cols]) | Create a Model from a formula and dataframe. |
get_distribution(params, scale[, exog, ...]) | Returns a random number generator for the predictive distribution. |
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. |
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, ...]) | Returns a random number generator for the predictive distribution. |
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 | Names of endogenous variables |
exog_names | Names of exogenous variables |