M/Ph7dZddlZddlZddlmZddlmZddlmZddl m Z ddl m Z m Z mZddlmZmZGdd e Zd ZiZd ed <d ed<ded<ejed<eed<ddZdZddZGdde ZdS)aM Quantile regression model Model parameters are estimated using iterated reweighted least squares. The asymptotic covariance matrix estimated using kernel density estimation. Author: Vincent Arel-Bundock License: BSD-3 Created: 2013-03-19 The original IRLS function was written for Matlab by Shapour Mohammadi, University of Tehran, 2008 (shmohammadi@gmail.com), with some lines based on code written by James P. Lesage in Applied Econometrics Using MATLAB(1999).PP. 73-4. Translated to python with permission from original author by Christian Prinoth (christian at prinoth dot name). N)pinv)norm)cache_readonly)RegressionModelRegressionResultsRegressionResultsWrapper)ConvergenceWarningIterationLimitWarningc4eZdZdZfdZdZ d d ZxZS) QuantRegaQuantile 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. c h||tj||fi|dSN) _check_kwargssuper__init__)selfendogexogkwargs __class__s j/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/regression/quantile_regression.pyrzQuantReg.__init__Ms> 6"""///////c|S)zE QuantReg model whitener does nothing: returns data. )rdatas rwhitenzQuantReg.whitenQs  r?robustepa hsheatherư>c |dks|dkrtdgd}||vr%tdd|zt|}|dkrt}n+|dkrt}n|d krt }ntd |j} |j} |j} tj |j} | |_ t|j |jz |_|j|j z |_d} | }tj| jd}d }d }t'gg }| |kr||kr|s| dz } |}tj|j| }tj|j| }tjt-||}| tj| |z }tj|dk}||dkdzdz dz||<tj|dk||zd|z |z}tj|}| |ddtjfz }tjtj||z }|d||dtj||z| dkr_| dzdkrVt;dd D]E}tj||d| krd}t?j dtBnF| |kr ||kr|| |kr-t?j dtE|zdztF| tj| |z }tIj%|dtIj%|dz }|| |}tMtj'| |dz tQj)||ztQj)||z z z}d| |zz tj*|||z z}|dkrtj|dk||z dzd|z |z dz}t-tj| j| }tj| j|tjddfz| }||z|z}nO|dkr:d|z dz|zd|z zt-tj| j| z}ntdtW||| } || _,| | _-d|z | _.|| _/|| _0tc| S)!a Solve by Iterative Weighted Least Squares Parameters ---------- q : float Quantile must be strictly between 0 and 1 vcov : str, 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) kernel : str, kernel to use in the kernel density estimation for the asymptotic covariance matrix: - epa: Epanechnikov - cos: Cosine - gau: Gaussian - par: Parzene bandwidth : str, 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) rz"q must be strictly between 0 and 1)biwcosrgauparzkernel must be one of z, r bofinger chamberlainz;bandwidth must be in 'hsheather', 'bofinger', 'chamberlain' F)paramsmser"Nr,r-i,dTzConvergence cycle detectedzMaximum number of iterations (z ) reached.Kgq= ףp??riidzvcov must be 'robust' or 'iid')normalized_cov_params)2 Exceptionjoinkernels hall_sheatherr)r*rrnobsnplinalg matrix_rankrankfloat k_constantdf_modeldf_residonesshapedictdotTrabswherenewaxismaxappendmeanrangeallwarningswarnr strr statsscoreatpercentileminstdrppfsumQuantRegResultsq iterationssparsity bandwidthhistoryr)!rrYvcovkernelr\max_iterp_tolr kern_namesrrr9 exog_rankn_iterxstarbetadiffcycler]beta0xtxxtyresidmaskiieiqrehfhat0dxtxixtdxlfits! rfitz QuantReg.fitWs9B 66Q!VV@AA A888  # #4tyy7L7LLMM MV_F  # #%II * $ $ II - ' '#IIYZZ Z yyI))$)44  di$/9::  DI- wtz!}%%+++xD5LLL aKFE&$''C&%((C6$s))S))DBF4...E6%==7*D!$K1,1A5@E$KHUQYE AaC5=AAEF5MME5BJ//E6"&..//D H  $ $T * * * EN ! !"'%+"6"6 7 7 7# FSLA$5$52,,Bvdgh&7&<<== $ &BDVWWW)xD5LLL2 X   M:S]]J&'(= ? ? ? 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