417 lines
14 KiB
Python
417 lines
14 KiB
Python
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#!/usr/bin/env python
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'''
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Quantile regression model
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Model parameters are estimated using iterated reweighted least squares. The
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asymptotic covariance matrix estimated using kernel density estimation.
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Author: Vincent Arel-Bundock
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License: BSD-3
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Created: 2013-03-19
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The original IRLS function was written for Matlab by Shapour Mohammadi,
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University of Tehran, 2008 (shmohammadi@gmail.com), with some lines based on
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code written by James P. Lesage in Applied Econometrics Using MATLAB(1999).PP.
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73-4. Translated to python with permission from original author by Christian
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Prinoth (christian at prinoth dot name).
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'''
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import numpy as np
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import warnings
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import scipy.stats as stats
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from numpy.linalg import pinv
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from scipy.stats import norm
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from statsmodels.tools.decorators import cache_readonly
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from statsmodels.regression.linear_model import (RegressionModel,
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RegressionResults,
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RegressionResultsWrapper)
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from statsmodels.tools.sm_exceptions import (ConvergenceWarning,
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IterationLimitWarning)
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class QuantReg(RegressionModel):
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'''Quantile Regression
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Estimate a quantile regression model using iterative reweighted least
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squares.
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Parameters
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----------
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endog : array or dataframe
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endogenous/response variable
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exog : array or dataframe
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exogenous/explanatory variable(s)
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Notes
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-----
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The Least Absolute Deviation (LAD) estimator is a special case where
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quantile is set to 0.5 (q argument of the fit method).
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The asymptotic covariance matrix is estimated following the procedure in
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Greene (2008, p.407-408), using either the logistic or gaussian kernels
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(kernel argument of the fit method).
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References
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----------
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General:
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* Birkes, D. and Y. Dodge(1993). Alternative Methods of Regression, John Wiley and Sons.
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* Green,W. H. (2008). Econometric Analysis. Sixth Edition. International Student Edition.
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* Koenker, R. (2005). Quantile Regression. New York: Cambridge University Press.
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* LeSage, J. P.(1999). Applied Econometrics Using MATLAB,
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Kernels (used by the fit method):
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* Green (2008) Table 14.2
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Bandwidth selection (used by the fit method):
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* Bofinger, E. (1975). Estimation of a density function using order statistics. Australian Journal of Statistics 17: 1-17.
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* 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.
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* Hall, P., and S. Sheather. (1988). On the distribution of the Studentized quantile. Journal of the Royal Statistical Society, Series B 50: 381-391.
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Keywords: Least Absolute Deviation(LAD) Regression, Quantile Regression,
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Regression, Robust Estimation.
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'''
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def __init__(self, endog, exog, **kwargs):
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self._check_kwargs(kwargs)
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super().__init__(endog, exog, **kwargs)
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def whiten(self, data):
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"""
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QuantReg model whitener does nothing: returns data.
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"""
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return data
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def fit(self, q=.5, vcov='robust', kernel='epa', bandwidth='hsheather',
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max_iter=1000, p_tol=1e-6, **kwargs):
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"""
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Solve by Iterative Weighted Least Squares
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Parameters
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----------
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q : float
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Quantile must be strictly between 0 and 1
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vcov : str, method used to calculate the variance-covariance matrix
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of the parameters. Default is ``robust``:
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- robust : heteroskedasticity robust standard errors (as suggested
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in Greene 6th edition)
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- iid : iid errors (as in Stata 12)
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kernel : str, kernel to use in the kernel density estimation for the
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asymptotic covariance matrix:
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- epa: Epanechnikov
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- cos: Cosine
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- gau: Gaussian
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- par: Parzene
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bandwidth : str, Bandwidth selection method in kernel density
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estimation for asymptotic covariance estimate (full
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references in QuantReg docstring):
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- hsheather: Hall-Sheather (1988)
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- bofinger: Bofinger (1975)
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- chamberlain: Chamberlain (1994)
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"""
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if q <= 0 or q >= 1:
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raise Exception('q must be strictly between 0 and 1')
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kern_names = ['biw', 'cos', 'epa', 'gau', 'par']
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if kernel not in kern_names:
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raise Exception("kernel must be one of " + ', '.join(kern_names))
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else:
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kernel = kernels[kernel]
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if bandwidth == 'hsheather':
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bandwidth = hall_sheather
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elif bandwidth == 'bofinger':
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bandwidth = bofinger
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elif bandwidth == 'chamberlain':
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bandwidth = chamberlain
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else:
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raise Exception("bandwidth must be in 'hsheather', 'bofinger', 'chamberlain'")
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endog = self.endog
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exog = self.exog
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nobs = self.nobs
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exog_rank = np.linalg.matrix_rank(self.exog)
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self.rank = exog_rank
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self.df_model = float(self.rank - self.k_constant)
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self.df_resid = self.nobs - self.rank
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n_iter = 0
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xstar = exog
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beta = np.ones(exog.shape[1])
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# TODO: better start, initial beta is used only for convergence check
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# Note the following does not work yet,
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# the iteration loop always starts with OLS as initial beta
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# if start_params is not None:
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# if len(start_params) != rank:
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# raise ValueError('start_params has wrong length')
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# beta = start_params
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# else:
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# # start with OLS
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# beta = np.dot(np.linalg.pinv(exog), endog)
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diff = 10
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cycle = False
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history = dict(params = [], mse=[])
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while n_iter < max_iter and diff > p_tol and not cycle:
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n_iter += 1
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beta0 = beta
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xtx = np.dot(xstar.T, exog)
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xty = np.dot(xstar.T, endog)
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beta = np.dot(pinv(xtx), xty)
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resid = endog - np.dot(exog, beta)
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mask = np.abs(resid) < .000001
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resid[mask] = ((resid[mask] >= 0) * 2 - 1) * .000001
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resid = np.where(resid < 0, q * resid, (1-q) * resid)
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resid = np.abs(resid)
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xstar = exog / resid[:, np.newaxis]
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diff = np.max(np.abs(beta - beta0))
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history['params'].append(beta)
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history['mse'].append(np.mean(resid*resid))
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if (n_iter >= 300) and (n_iter % 100 == 0):
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# check for convergence circle, should not happen
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for ii in range(2, 10):
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if np.all(beta == history['params'][-ii]):
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cycle = True
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warnings.warn("Convergence cycle detected", ConvergenceWarning)
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break
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if n_iter == max_iter:
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warnings.warn("Maximum number of iterations (" + str(max_iter) +
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") reached.", IterationLimitWarning)
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e = endog - np.dot(exog, beta)
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# Greene (2008, p.407) writes that Stata 6 uses this bandwidth:
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# h = 0.9 * np.std(e) / (nobs**0.2)
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# Instead, we calculate bandwidth as in Stata 12
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iqre = stats.scoreatpercentile(e, 75) - stats.scoreatpercentile(e, 25)
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h = bandwidth(nobs, q)
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h = min(np.std(endog),
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iqre / 1.34) * (norm.ppf(q + h) - norm.ppf(q - h))
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fhat0 = 1. / (nobs * h) * np.sum(kernel(e / h))
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if vcov == 'robust':
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d = np.where(e > 0, (q/fhat0)**2, ((1-q)/fhat0)**2)
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xtxi = pinv(np.dot(exog.T, exog))
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xtdx = np.dot(exog.T * d[np.newaxis, :], exog)
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vcov = xtxi @ xtdx @ xtxi
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elif vcov == 'iid':
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vcov = (1. / fhat0)**2 * q * (1 - q) * pinv(np.dot(exog.T, exog))
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else:
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raise Exception("vcov must be 'robust' or 'iid'")
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lfit = QuantRegResults(self, beta, normalized_cov_params=vcov)
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lfit.q = q
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lfit.iterations = n_iter
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lfit.sparsity = 1. / fhat0
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lfit.bandwidth = h
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lfit.history = history
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return RegressionResultsWrapper(lfit)
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def _parzen(u):
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z = np.where(np.abs(u) <= .5, 4./3 - 8. * u**2 + 8. * np.abs(u)**3,
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8. * (1 - np.abs(u))**3 / 3.)
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z[np.abs(u) > 1] = 0
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return z
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kernels = {}
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kernels['biw'] = lambda u: 15. / 16 * (1 - u**2)**2 * np.where(np.abs(u) <= 1, 1, 0)
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kernels['cos'] = lambda u: np.where(np.abs(u) <= .5, 1 + np.cos(2 * np.pi * u), 0)
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kernels['epa'] = lambda u: 3. / 4 * (1-u**2) * np.where(np.abs(u) <= 1, 1, 0)
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kernels['gau'] = norm.pdf
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kernels['par'] = _parzen
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#kernels['bet'] = lambda u: np.where(np.abs(u) <= 1, .75 * (1 - u) * (1 + u), 0)
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#kernels['log'] = lambda u: logistic.pdf(u) * (1 - logistic.pdf(u))
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#kernels['tri'] = lambda u: np.where(np.abs(u) <= 1, 1 - np.abs(u), 0)
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#kernels['trw'] = lambda u: 35. / 32 * (1 - u**2)**3 * np.where(np.abs(u) <= 1, 1, 0)
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#kernels['uni'] = lambda u: 1. / 2 * np.where(np.abs(u) <= 1, 1, 0)
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def hall_sheather(n, q, alpha=.05):
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z = norm.ppf(q)
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num = 1.5 * norm.pdf(z)**2.
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den = 2. * z**2. + 1.
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h = n**(-1. / 3) * norm.ppf(1. - alpha / 2.)**(2./3) * (num / den)**(1./3)
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return h
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def bofinger(n, q):
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num = 9. / 2 * norm.pdf(2 * norm.ppf(q))**4
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den = (2 * norm.ppf(q)**2 + 1)**2
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h = n**(-1. / 5) * (num / den)**(1. / 5)
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return h
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def chamberlain(n, q, alpha=.05):
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return norm.ppf(1 - alpha / 2) * np.sqrt(q*(1 - q) / n)
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class QuantRegResults(RegressionResults):
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'''Results instance for the QuantReg model'''
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@cache_readonly
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def prsquared(self):
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q = self.q
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endog = self.model.endog
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e = self.resid
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e = np.where(e < 0, (1 - q) * e, q * e)
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e = np.abs(e)
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ered = endog - stats.scoreatpercentile(endog, q * 100)
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ered = np.where(ered < 0, (1 - q) * ered, q * ered)
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ered = np.abs(ered)
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return 1 - np.sum(e) / np.sum(ered)
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#@cache_readonly
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def scale(self):
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return 1.
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@cache_readonly
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def bic(self):
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return np.nan
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@cache_readonly
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def aic(self):
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return np.nan
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@cache_readonly
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def llf(self):
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return np.nan
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@cache_readonly
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def rsquared(self):
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return np.nan
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@cache_readonly
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def rsquared_adj(self):
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return np.nan
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@cache_readonly
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def mse(self):
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return np.nan
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@cache_readonly
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def mse_model(self):
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return np.nan
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@cache_readonly
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def mse_total(self):
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return np.nan
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@cache_readonly
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def centered_tss(self):
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return np.nan
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@cache_readonly
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def uncentered_tss(self):
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return np.nan
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@cache_readonly
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def HC0_se(self):
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raise NotImplementedError
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@cache_readonly
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def HC1_se(self):
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raise NotImplementedError
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@cache_readonly
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def HC2_se(self):
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raise NotImplementedError
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@cache_readonly
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def HC3_se(self):
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raise NotImplementedError
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def summary(self, yname=None, xname=None, title=None, alpha=.05):
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"""Summarize the Regression Results
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Parameters
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----------
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yname : str, optional
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Default is `y`
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xname : list[str], optional
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Names for the exogenous variables. Default is `var_##` for ## in
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the number of regressors. Must match the number of parameters
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in the model
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title : str, optional
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Title for the top table. If not None, then this replaces the
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default title
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alpha : float
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significance level for the confidence intervals
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Returns
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-------
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smry : Summary instance
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this holds the summary tables and text, which can be printed or
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converted to various output formats.
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See Also
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--------
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statsmodels.iolib.summary.Summary : class to hold summary results
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"""
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eigvals = self.eigenvals
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condno = self.condition_number
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top_left = [('Dep. Variable:', None),
|
||
|
|
('Model:', None),
|
||
|
|
('Method:', ['Least Squares']),
|
||
|
|
('Date:', None),
|
||
|
|
('Time:', None)
|
||
|
|
]
|
||
|
|
|
||
|
|
top_right = [('Pseudo R-squared:', ["%#8.4g" % self.prsquared]),
|
||
|
|
('Bandwidth:', ["%#8.4g" % self.bandwidth]),
|
||
|
|
('Sparsity:', ["%#8.4g" % self.sparsity]),
|
||
|
|
('No. Observations:', None),
|
||
|
|
('Df Residuals:', None),
|
||
|
|
('Df Model:', None)
|
||
|
|
]
|
||
|
|
|
||
|
|
if title is None:
|
||
|
|
title = self.model.__class__.__name__ + ' ' + "Regression Results"
|
||
|
|
|
||
|
|
# create summary table instance
|
||
|
|
from statsmodels.iolib.summary import Summary
|
||
|
|
smry = Summary()
|
||
|
|
smry.add_table_2cols(self, gleft=top_left, gright=top_right,
|
||
|
|
yname=yname, xname=xname, title=title)
|
||
|
|
smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
|
||
|
|
use_t=self.use_t)
|
||
|
|
|
||
|
|
# add warnings/notes, added to text format only
|
||
|
|
etext = []
|
||
|
|
if eigvals[-1] < 1e-10:
|
||
|
|
wstr = "The smallest eigenvalue is %6.3g. This might indicate "
|
||
|
|
wstr += "that there are\n"
|
||
|
|
wstr += "strong multicollinearity problems or that the design "
|
||
|
|
wstr += "matrix is singular."
|
||
|
|
wstr = wstr % eigvals[-1]
|
||
|
|
etext.append(wstr)
|
||
|
|
elif condno > 1000: # TODO: what is recommended
|
||
|
|
wstr = "The condition number is large, %6.3g. This might "
|
||
|
|
wstr += "indicate that there are\n"
|
||
|
|
wstr += "strong multicollinearity or other numerical "
|
||
|
|
wstr += "problems."
|
||
|
|
wstr = wstr % condno
|
||
|
|
etext.append(wstr)
|
||
|
|
|
||
|
|
if etext:
|
||
|
|
smry.add_extra_txt(etext)
|
||
|
|
|
||
|
|
return smry
|