U md7@sdZddlZddlZddlmZddlmZddlmZddl m Z ddl m Z m Z mZddlmZmZGdd d e Zd d ZiZd d ed<dd ed<dd ed<ejed<eed<dddZddZdddZGddde 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)ConvergenceWarningIterationLimitWarningcs2eZdZdZfddZddZdd d ZZS)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 s$||tt|j||f|dSN)Z _check_kwargssuperr __init__)selfendogexogkwargs __class__c/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/statsmodels/regression/quantile_regression.pyr Ms zQuantReg.__init__cCs|S)zE QuantReg model whitener does nothing: returns data. r)rdatarrrwhitenQszQuantReg.whiten?robustepa hsheatherư>c!Ks|dks|dkrtddddddg}||krBtd d |nt|}|d krXt}n$|d krft}n|d krtt}ntd|j} |j} |j} t j |j} | |_ t |j |j|_|j|j |_d} | }t | jd}d}d}tggd}| |krH||krH|sH| d7} |}t |j| }t |j| }t t||}| t | |}t |dk}||dkddd||<t |dk||d||}t |}| |ddt jf}t t ||}|d||dt ||| dkr| ddkrtddD]4}t ||d| krd}t dt!qqq| |krjt dt"|dt#| t | |}t$%|dt$%|d}|| |}t&t '| |dt()||t()||}d | |t *|||}|d!krZt |dk||dd||d}tt | j| }t | j|t jddf| }|||}n>|d"krd |d|d|tt | j| }ntd#t+|||d$} || _,| | _-d || _.|| _/|| _0t1| S)%a Solve by Iterative Weighted Least Squares Parameters ---------- q : float Quantile must be strictly between 0 and 1 vcov : str, method used to calculate the variance-covariance matrix of the parameters. Default is ``robust``: - robust : heteroskedasticity robust standard errors (as suggested in Greene 6th edition) - iid : iid errors (as in Stata 12) kernel : str, kernel to use in the kernel density estimation for the asymptotic covariance matrix: - epa: Epanechnikov - cos: Cosine - gau: Gaussian - par: Parzene bandwidth : str, Bandwidth selection method in kernel density estimation for asymptotic covariance estimate (full references in QuantReg docstring): - hsheather: Hall-Sheather (1988) - bofinger: Bofinger (1975) - chamberlain: Chamberlain (1994) rz"q must be strictly between 0 and 1biwcosrgauparzkernel must be one of z, rbofinger chamberlainz;bandwidth must be in 'hsheather', 'bofinger', 'chamberlain' F)paramsmserNr&r'i,dTzConvergence cycle detectedzMaximum number of iterations (z ) reached.Kgq= ףp??rZiidzvcov must be 'robust' or 'iid')Znormalized_cov_params)2 Exceptionjoinkernels hall_sheatherr#r$rrnobsnpZlinalgZ matrix_rankZrankfloatZ k_constantZdf_modelZdf_residZonesshapedictdotTrabswhereZnewaxismaxappendZmeanrangeallwarningswarnrstrr statsscoreatpercentileminZstdrppfsumQuantRegResultsqZ iterationssparsity bandwidthhistoryr)!rrGZvcovZkernelrIZmax_iterZp_tolrZ kern_namesrrr1Z exog_rankZn_iterZxstarbetadiffcyclerJZbeta0ZxtxZxtyresidmaskiieZiqrehZfhat0dZxtxiZxtdxZlfitrrrfitWs!         &  , z QuantReg.fit)rrrrrr)__name__ __module__ __qualname____doc__r rrT __classcell__rrrrr s, r c Csbtt|dkdd|ddt|dddt|dd}d|t|dk<|S) NrgUUUUUU?g @r(rg@rr2r9r8)uzrrr_parzens 0r^cCs,dd|ddtt|dkddS)Ng?rr(rr[r\rrrr`rcCs,tt|dkdtdtj|dS)Nrrr(r)r2r9r8r pir_rrrr`rar cCs(dd|dtt|dkddS)Ng?rr(rr[r_rrrr`rarr!r"皙?cCsZt|}dt|d}d|dd}|dtd|dd||d}|S)Ng?g@r,gUUUUUUտgUUUUUU?gUUUUUU?)rrDpdf)nrGalphar]numdenrRrrrr0s  *r0cCsNdtdt|d}dt|ddd}|d||d}|S)Ng@r(rgɿg?)rrdrD)rerGrgrhrRrrrr#sr#cCs(td|dt|d||S)Nrr()rrDr2sqrt)rerGrfrrrr$sr$c@seZdZdZeddZddZeddZedd Zed d Z ed d Z eddZ eddZ eddZ eddZeddZeddZeddZeddZeddZed d!Zd&d$d%Zd"S)'rFz'Results instance for the QuantReg modelcCs|j}|jj}|j}t|dkd||||}t|}|t||d}t|dkd||||}t|}dt |t |S)Nrrr)) rGmodelrrNr2r9r8rArBrE)rrGrrQZeredrrr prsquared s  zQuantRegResults.prsquaredcCsdS)Nr,rrrrrscaleszQuantRegResults.scalecCstjSr r2nanrmrrrbicszQuantRegResults.biccCstjSr rormrrraic szQuantRegResults.aiccCstjSr rormrrrllf$szQuantRegResults.llfcCstjSr rormrrrrsquared(szQuantRegResults.rsquaredcCstjSr rormrrr rsquared_adj,szQuantRegResults.rsquared_adjcCstjSr rormrrrr'0szQuantRegResults.msecCstjSr rormrrr mse_model4szQuantRegResults.mse_modelcCstjSr rormrrr mse_total8szQuantRegResults.mse_totalcCstjSr rormrrr centered_tss<szQuantRegResults.centered_tsscCstjSr rormrrruncentered_tss@szQuantRegResults.uncentered_tsscCstdSr NotImplementedErrorrmrrrHC0_seDszQuantRegResults.HC0_secCstdSr rzrmrrrHC1_seHszQuantRegResults.HC1_secCstdSr rzrmrrrHC2_seLszQuantRegResults.HC2_secCstdSr rzrmrrrHC3_sePszQuantRegResults.HC3_seNrcc Cs<|j}|j}ddddgfddg}dd|jgfd d|jgfd d|jgfd d d g}|dkrn|jjjdd}ddlm } | } | j ||||||d| j |||||j dg} |ddkrd} | d7} | d7} | d7} | |d} | | n8|dkr(d} | d7} | d7} | d7} | |} | | | r8| | | S) a[Summarize the Regression Results Parameters ---------- yname : str, optional Default is `y` xname : list[str], optional Names for the exogenous variables. Default is `var_##` for ## in the number of regressors. Must match the number of parameters in the model title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns ------- smry : Summary instance this holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results )zDep. Variable:N)zModel:NzMethod:z Least Squares)zDate:N)zTime:NzPseudo R-squared:z%#8.4gz Bandwidth:z Sparsity:)zNo. Observations:N)z Df Residuals:N)z Df Model:NN zRegression Resultsr)Summary)ZgleftZgrightynamexnametitle)rrrfuse_tg|=z6The smallest eigenvalue is %6.3g. This might indicate zthat there are z5strong multicollinearity problems or that the design zmatrix is singular.rz1The condition number is large, %6.3g. 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