Source code for statsmodels.nonparametric.kernel_regression

"""
Multivariate Conditional and Unconditional Kernel Density Estimation
with Mixed Data Types

References
----------
[1] Racine, J., Li, Q. Nonparametric econometrics: theory and practice.
    Princeton University Press. (2007)
[2] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation
    and Trends in Econometrics: Vol 3: No 1, pp1-88. (2008)
    http://dx.doi.org/10.1561/0800000009
[3] Racine, J., Li, Q. "Nonparametric Estimation of Distributions
    with Categorical and Continuous Data." Working Paper. (2000)
[4] Racine, J. Li, Q. "Kernel Estimation of Multivariate Conditional
    Distributions Annals of Economics and Finance 5, 211-235 (2004)
[5] Liu, R., Yang, L. "Kernel estimation of multivariate
    cumulative distribution function."
    Journal of Nonparametric Statistics (2008)
[6] Li, R., Ju, G. "Nonparametric Estimation of Multivariate CDF
    with Categorical and Continuous Data." Working Paper
[7] Li, Q., Racine, J. "Cross-validated local linear nonparametric
    regression" Statistica Sinica 14(2004), pp. 485-512
[8] Racine, J.: "Consistent Significance Testing for Nonparametric
        Regression" Journal of Business & Economics Statistics
[9] Racine, J., Hart, J., Li, Q., "Testing the Significance of
        Categorical Predictor Variables in Nonparametric Regression
        Models", 2006, Econometric Reviews 25, 523-544

"""

from __future__ import division
# TODO: make default behavior efficient=True above a certain n_obs

from statsmodels.compat.python import range, string_types, next
import copy

import numpy as np
from scipy import optimize
from scipy.stats.mstats import mquantiles

from ._kernel_base import GenericKDE, EstimatorSettings, gpke, \
    LeaveOneOut, _get_type_pos, _adjust_shape, _compute_min_std_IQR


__all__ = ['KernelReg', 'KernelCensoredReg']


[docs]class KernelReg(GenericKDE): """ Nonparametric kernel regression class. Calculates the conditional mean ``E[y|X]`` where ``y = g(X) + e``. Note that the "local constant" type of regression provided here is also known as Nadaraya-Watson kernel regression; "local linear" is an extension of that which suffers less from bias issues at the edge of the support. Parameters ---------- endog: array-like This is the dependent variable. exog: array-like The training data for the independent variable(s) Each element in the list is a separate variable var_type: str The type of the variables, one character per variable: - c: continuous - u: unordered (discrete) - o: ordered (discrete) reg_type: {'lc', 'll'}, optional Type of regression estimator. 'lc' means local constant and 'll' local Linear estimator. Default is 'll' bw: str or array_like, optional Either a user-specified bandwidth or the method for bandwidth selection. If a string, valid values are 'cv_ls' (least-squares cross-validation) and 'aic' (AIC Hurvich bandwidth estimation). Default is 'cv_ls'. User specified bandwidth must have as many entries as the number of variables. defaults: EstimatorSettings instance, optional The default values for the efficient bandwidth estimation. Attributes --------- bw: array_like The bandwidth parameters. """ def __init__(self, endog, exog, var_type, reg_type='ll', bw='cv_ls', defaults=None): self.var_type = var_type self.data_type = var_type self.reg_type = reg_type self.k_vars = len(self.var_type) self.endog = _adjust_shape(endog, 1) self.exog = _adjust_shape(exog, self.k_vars) self.data = np.column_stack((self.endog, self.exog)) self.nobs = np.shape(self.exog)[0] self.est = dict(lc=self._est_loc_constant, ll=self._est_loc_linear) defaults = EstimatorSettings() if defaults is None else defaults self._set_defaults(defaults) if not isinstance(bw, string_types): bw = np.asarray(bw) if len(bw) != self.k_vars: raise ValueError('bw must have the same dimension as the ' 'number of variables.') if not self.efficient: self.bw = self._compute_reg_bw(bw) else: self.bw = self._compute_efficient(bw) def _compute_reg_bw(self, bw): if not isinstance(bw, string_types): self._bw_method = "user-specified" return np.asarray(bw) else: # The user specified a bandwidth selection method e.g. 'cv_ls' self._bw_method = bw # Workaround to avoid instance methods in __dict__ if bw == 'cv_ls': res = self.cv_loo else: # bw == 'aic' res = self.aic_hurvich X = np.std(self.exog, axis=0) h0 = 1.06 * X * \ self.nobs ** (- 1. / (4 + np.size(self.exog, axis=1))) func = self.est[self.reg_type] bw_estimated = optimize.fmin(res, x0=h0, args=(func, ), maxiter=1e3, maxfun=1e3, disp=0) return bw_estimated def _est_loc_linear(self, bw, endog, exog, data_predict): """ Local linear estimator of g(x) in the regression ``y = g(x) + e``. Parameters ---------- bw: array_like Vector of bandwidth value(s). endog: 1D array_like The dependent variable. exog: 1D or 2D array_like The independent variable(s). data_predict: 1D array_like of length K, where K is the number of variables. The point at which the density is estimated. Returns ------- D_x: array_like The value of the conditional mean at `data_predict`. Notes ----- See p. 81 in [1] and p.38 in [2] for the formulas. Unlike other methods, this one requires that `data_predict` be 1D. """ nobs, k_vars = exog.shape ker = gpke(bw, data=exog, data_predict=data_predict, var_type=self.var_type, #ukertype='aitchison_aitken_reg', #okertype='wangryzin_reg', tosum=False) / float(nobs) # Create the matrix on p.492 in [7], after the multiplication w/ K_h,ij # See also p. 38 in [2] #ix_cont = np.arange(self.k_vars) # Use all vars instead of continuous only # Note: because ix_cont was defined here such that it selected all # columns, I removed the indexing with it from exog/data_predict. # Convert ker to a 2-D array to make matrix operations below work ker = ker[:, np.newaxis] M12 = exog - data_predict M22 = np.dot(M12.T, M12 * ker) M12 = (M12 * ker).sum(axis=0) M = np.empty((k_vars + 1, k_vars + 1)) M[0, 0] = ker.sum() M[0, 1:] = M12 M[1:, 0] = M12 M[1:, 1:] = M22 ker_endog = ker * endog V = np.empty((k_vars + 1, 1)) V[0, 0] = ker_endog.sum() V[1:, 0] = ((exog - data_predict) * ker_endog).sum(axis=0) mean_mfx = np.dot(np.linalg.pinv(M), V) mean = mean_mfx[0] mfx = mean_mfx[1:, :] return mean, mfx def _est_loc_constant(self, bw, endog, exog, data_predict): """ Local constant estimator of g(x) in the regression y = g(x) + e Parameters ---------- bw : array_like Array of bandwidth value(s). endog : 1D array_like The dependent variable. exog : 1D or 2D array_like The independent variable(s). data_predict : 1D or 2D array_like The point(s) at which the density is estimated. Returns ------- G : ndarray The value of the conditional mean at `data_predict`. B_x : ndarray The marginal effects. """ ker_x = gpke(bw, data=exog, data_predict=data_predict, var_type=self.var_type, #ukertype='aitchison_aitken_reg', #okertype='wangryzin_reg', tosum=False) ker_x = np.reshape(ker_x, np.shape(endog)) G_numer = (ker_x * endog).sum(axis=0) G_denom = ker_x.sum(axis=0) G = G_numer / G_denom nobs = exog.shape[0] f_x = G_denom / float(nobs) ker_xc = gpke(bw, data=exog, data_predict=data_predict, var_type=self.var_type, ckertype='d_gaussian', #okertype='wangryzin_reg', tosum=False) ker_xc = ker_xc[:, np.newaxis] d_mx = -(endog * ker_xc).sum(axis=0) / float(nobs) #* np.prod(bw[:, ix_cont])) d_fx = -ker_xc.sum(axis=0) / float(nobs) #* np.prod(bw[:, ix_cont])) B_x = d_mx / f_x - G * d_fx / f_x B_x = (G_numer * d_fx - G_denom * d_mx) / (G_denom**2) #B_x = (f_x * d_mx - m_x * d_fx) / (f_x ** 2) return G, B_x
[docs] def aic_hurvich(self, bw, func=None): """ Computes the AIC Hurvich criteria for the estimation of the bandwidth. Parameters ---------- bw : str or array_like See the ``bw`` parameter of `KernelReg` for details. Returns ------- aic : ndarray The AIC Hurvich criteria, one element for each variable. func : None Unused here, needed in signature because it's used in `cv_loo`. References ---------- See ch.2 in [1] and p.35 in [2]. """ H = np.empty((self.nobs, self.nobs)) for j in range(self.nobs): H[:, j] = gpke(bw, data=self.exog, data_predict=self.exog[j,:], var_type=self.var_type, tosum=False) denom = H.sum(axis=1) H = H / denom gx = KernelReg(endog=self.endog, exog=self.exog, var_type=self.var_type, reg_type=self.reg_type, bw=bw, defaults=EstimatorSettings(efficient=False)).fit()[0] gx = np.reshape(gx, (self.nobs, 1)) sigma = ((self.endog - gx)**2).sum(axis=0) / float(self.nobs) frac = (1 + np.trace(H) / float(self.nobs)) / \ (1 - (np.trace(H) + 2) / float(self.nobs)) #siga = np.dot(self.endog.T, (I - H).T) #sigb = np.dot((I - H), self.endog) #sigma = np.dot(siga, sigb) / float(self.nobs) aic = np.log(sigma) + frac return aic
[docs] def cv_loo(self, bw, func): r""" The cross-validation function with leave-one-out estimator. Parameters ---------- bw: array_like Vector of bandwidth values. func: callable function Returns the estimator of g(x). Can be either ``_est_loc_constant`` (local constant) or ``_est_loc_linear`` (local_linear). Returns ------- L: float The value of the CV function. Notes ----- Calculates the cross-validation least-squares function. This function is minimized by compute_bw to calculate the optimal value of `bw`. For details see p.35 in [2] .. math:: CV(h)=n^{-1}\sum_{i=1}^{n}(Y_{i}-g_{-i}(X_{i}))^{2} where :math:`g_{-i}(X_{i})` is the leave-one-out estimator of g(X) and :math:`h` is the vector of bandwidths """ LOO_X = LeaveOneOut(self.exog) LOO_Y = LeaveOneOut(self.endog).__iter__() L = 0 for ii, X_not_i in enumerate(LOO_X): Y = next(LOO_Y) G = func(bw, endog=Y, exog=-X_not_i, data_predict=-self.exog[ii, :])[0] L += (self.endog[ii] - G) ** 2 # Note: There might be a way to vectorize this. See p.72 in [1] return L / self.nobs
[docs] def r_squared(self): r""" Returns the R-Squared for the nonparametric regression. Notes ----- For more details see p.45 in [2] The R-Squared is calculated by: .. math:: R^{2}=\frac{\left[\sum_{i=1}^{n} (Y_{i}-\bar{y})(\hat{Y_{i}}-\bar{y}\right]^{2}}{\sum_{i=1}^{n} (Y_{i}-\bar{y})^{2}\sum_{i=1}^{n}(\hat{Y_{i}}-\bar{y})^{2}}, where :math:`\hat{Y_{i}}` is the mean calculated in `fit` at the exog points. """ Y = np.squeeze(self.endog) Yhat = self.fit()[0] Y_bar = np.mean(Yhat) R2_numer = (((Y - Y_bar) * (Yhat - Y_bar)).sum())**2 R2_denom = ((Y - Y_bar)**2).sum(axis=0) * \ ((Yhat - Y_bar)**2).sum(axis=0) return R2_numer / R2_denom
[docs] def fit(self, data_predict=None): """ Returns the mean and marginal effects at the `data_predict` points. Parameters ---------- data_predict : array_like, optional Points at which to return the mean and marginal effects. If not given, ``data_predict == exog``. Returns ------- mean : ndarray The regression result for the mean (i.e. the actual curve). mfx : ndarray The marginal effects, i.e. the partial derivatives of the mean. """ func = self.est[self.reg_type] if data_predict is None: data_predict = self.exog else: data_predict = _adjust_shape(data_predict, self.k_vars) N_data_predict = np.shape(data_predict)[0] mean = np.empty((N_data_predict,)) mfx = np.empty((N_data_predict, self.k_vars)) for i in range(N_data_predict): mean_mfx = func(self.bw, self.endog, self.exog, data_predict=data_predict[i, :]) mean[i] = mean_mfx[0] mfx_c = np.squeeze(mean_mfx[1]) mfx[i, :] = mfx_c return mean, mfx
[docs] def sig_test(self, var_pos, nboot=50, nested_res=25, pivot=False): """ Significance test for the variables in the regression. Parameters ---------- var_pos: sequence The position of the variable in exog to be tested. Returns ------- sig: str The level of significance: - `*` : at 90% confidence level - `**` : at 95% confidence level - `***` : at 99* confidence level - "Not Significant" : if not significant """ var_pos = np.asarray(var_pos) ix_cont, ix_ord, ix_unord = _get_type_pos(self.var_type) if np.any(ix_cont[var_pos]): if np.any(ix_ord[var_pos]) or np.any(ix_unord[var_pos]): raise ValueError("Discrete variable in hypothesis. Must be continuous") Sig = TestRegCoefC(self, var_pos, nboot, nested_res, pivot) else: Sig = TestRegCoefD(self, var_pos, nboot) return Sig.sig
def __repr__(self): """Provide something sane to print.""" rpr = "KernelReg instance\n" rpr += "Number of variables: k_vars = " + str(self.k_vars) + "\n" rpr += "Number of samples: N = " + str(self.nobs) + "\n" rpr += "Variable types: " + self.var_type + "\n" rpr += "BW selection method: " + self._bw_method + "\n" rpr += "Estimator type: " + self.reg_type + "\n" return rpr def _get_class_vars_type(self): """Helper method to be able to pass needed vars to _compute_subset.""" class_type = 'KernelReg' class_vars = (self.var_type, self.k_vars, self.reg_type) return class_type, class_vars def _compute_dispersion(self, data): """ Computes the measure of dispersion. The minimum of the standard deviation and interquartile range / 1.349 References ---------- See the user guide for the np package in R. In the notes on bwscaling option in npreg, npudens, npcdens there is a discussion on the measure of dispersion """ data = data[:, 1:] return _compute_min_std_IQR(data)
[docs]class KernelCensoredReg(KernelReg): """ Nonparametric censored regression. Calculates the condtional mean ``E[y|X]`` where ``y = g(X) + e``, where y is left-censored. Left censored variable Y is defined as ``Y = min {Y', L}`` where ``L`` is the value at which ``Y`` is censored and ``Y'`` is the true value of the variable. Parameters ---------- endog: list with one element which is array_like This is the dependent variable. exog: list The training data for the independent variable(s) Each element in the list is a separate variable dep_type: str The type of the dependent variable(s) c: Continuous u: Unordered (Discrete) o: Ordered (Discrete) reg_type: str Type of regression estimator lc: Local Constant Estimator ll: Local Linear Estimator bw: array_like Either a user-specified bandwidth or the method for bandwidth selection. cv_ls: cross-validaton least squares aic: AIC Hurvich Estimator censor_val: float Value at which the dependent variable is censored defaults: EstimatorSettings instance, optional The default values for the efficient bandwidth estimation Attributes --------- bw: array_like The bandwidth parameters """ def __init__(self, endog, exog, var_type, reg_type, bw='cv_ls', censor_val=0, defaults=None): self.var_type = var_type self.data_type = var_type self.reg_type = reg_type self.k_vars = len(self.var_type) self.endog = _adjust_shape(endog, 1) self.exog = _adjust_shape(exog, self.k_vars) self.data = np.column_stack((self.endog, self.exog)) self.nobs = np.shape(self.exog)[0] self.est = dict(lc=self._est_loc_constant, ll=self._est_loc_linear) defaults = EstimatorSettings() if defaults is None else defaults self._set_defaults(defaults) self.censor_val = censor_val if self.censor_val is not None: self.censored(censor_val) else: self.W_in = np.ones((self.nobs, 1)) if not self.efficient: self.bw = self._compute_reg_bw(bw) else: self.bw = self._compute_efficient(bw)
[docs] def censored(self, censor_val): # see pp. 341-344 in [1] self.d = (self.endog != censor_val) * 1. ix = np.argsort(np.squeeze(self.endog)) self.sortix = ix self.sortix_rev = np.zeros(ix.shape, int) self.sortix_rev[ix] = np.arange(len(ix)) self.endog = np.squeeze(self.endog[ix]) self.endog = _adjust_shape(self.endog, 1) self.exog = np.squeeze(self.exog[ix]) self.d = np.squeeze(self.d[ix]) self.W_in = np.empty((self.nobs, 1)) for i in range(1, self.nobs + 1): P=1 for j in range(1, i): P *= ((self.nobs - j)/(float(self.nobs)-j+1))**self.d[j-1] self.W_in[i-1,0] = P * self.d[i-1] / (float(self.nobs) - i + 1 )
def __repr__(self): """Provide something sane to print.""" rpr = "KernelCensoredReg instance\n" rpr += "Number of variables: k_vars = " + str(self.k_vars) + "\n" rpr += "Number of samples: nobs = " + str(self.nobs) + "\n" rpr += "Variable types: " + self.var_type + "\n" rpr += "BW selection method: " + self._bw_method + "\n" rpr += "Estimator type: " + self.reg_type + "\n" return rpr def _est_loc_linear(self, bw, endog, exog, data_predict, W): """ Local linear estimator of g(x) in the regression ``y = g(x) + e``. Parameters ---------- bw: array_like Vector of bandwidth value(s) endog: 1D array_like The dependent variable exog: 1D or 2D array_like The independent variable(s) data_predict: 1D array_like of length K, where K is the number of variables. The point at which the density is estimated Returns ------- D_x: array_like The value of the conditional mean at data_predict Notes ----- See p. 81 in [1] and p.38 in [2] for the formulas Unlike other methods, this one requires that data_predict be 1D """ nobs, k_vars = exog.shape ker = gpke(bw, data=exog, data_predict=data_predict, var_type=self.var_type, ukertype='aitchison_aitken_reg', okertype='wangryzin_reg', tosum=False) # Create the matrix on p.492 in [7], after the multiplication w/ K_h,ij # See also p. 38 in [2] # Convert ker to a 2-D array to make matrix operations below work ker = W * ker[:, np.newaxis] M12 = exog - data_predict M22 = np.dot(M12.T, M12 * ker) M12 = (M12 * ker).sum(axis=0) M = np.empty((k_vars + 1, k_vars + 1)) M[0, 0] = ker.sum() M[0, 1:] = M12 M[1:, 0] = M12 M[1:, 1:] = M22 ker_endog = ker * endog V = np.empty((k_vars + 1, 1)) V[0, 0] = ker_endog.sum() V[1:, 0] = ((exog - data_predict) * ker_endog).sum(axis=0) mean_mfx = np.dot(np.linalg.pinv(M), V) mean = mean_mfx[0] mfx = mean_mfx[1:, :] return mean, mfx
[docs] def cv_loo(self, bw, func): r""" The cross-validation function with leave-one-out estimator Parameters ---------- bw: array_like Vector of bandwidth values func: callable function Returns the estimator of g(x). Can be either ``_est_loc_constant`` (local constant) or ``_est_loc_linear`` (local_linear). Returns ------- L: float The value of the CV function Notes ----- Calculates the cross-validation least-squares function. This function is minimized by compute_bw to calculate the optimal value of bw For details see p.35 in [2] .. math:: CV(h)=n^{-1}\sum_{i=1}^{n}(Y_{i}-g_{-i}(X_{i}))^{2} where :math:`g_{-i}(X_{i})` is the leave-one-out estimator of g(X) and :math:`h` is the vector of bandwidths """ LOO_X = LeaveOneOut(self.exog) LOO_Y = LeaveOneOut(self.endog).__iter__() LOO_W = LeaveOneOut(self.W_in).__iter__() L = 0 for ii, X_not_i in enumerate(LOO_X): Y = next(LOO_Y) w = next(LOO_W) G = func(bw, endog=Y, exog=-X_not_i, data_predict=-self.exog[ii, :], W=w)[0] L += (self.endog[ii] - G) ** 2 # Note: There might be a way to vectorize this. See p.72 in [1] return L / self.nobs
[docs] def fit(self, data_predict=None): """ Returns the marginal effects at the data_predict points. """ func = self.est[self.reg_type] if data_predict is None: data_predict = self.exog else: data_predict = _adjust_shape(data_predict, self.k_vars) N_data_predict = np.shape(data_predict)[0] mean = np.empty((N_data_predict,)) mfx = np.empty((N_data_predict, self.k_vars)) for i in range(N_data_predict): mean_mfx = func(self.bw, self.endog, self.exog, data_predict=data_predict[i, :], W=self.W_in) mean[i] = mean_mfx[0] mfx_c = np.squeeze(mean_mfx[1]) mfx[i, :] = mfx_c return mean, mfx
class TestRegCoefC(object): """ Significance test for continuous variables in a nonparametric regression. The null hypothesis is ``dE(Y|X)/dX_not_i = 0``, the alternative hypothesis is ``dE(Y|X)/dX_not_i != 0``. Parameters ---------- model: KernelReg instance This is the nonparametric regression model whose elements are tested for significance. test_vars: tuple, list of integers, array_like index of position of the continuous variables to be tested for significance. E.g. (1,3,5) jointly tests variables at position 1,3 and 5 for significance. nboot: int Number of bootstrap samples used to determine the distribution of the test statistic in a finite sample. Default is 400 nested_res: int Number of nested resamples used to calculate lambda. Must enable the pivot option pivot: bool Pivot the test statistic by dividing by its standard error Significantly increases computational time. But pivot statistics have more desirable properties (See references) Attributes ---------- sig: str The significance level of the variable(s) tested "Not Significant": Not significant at the 90% confidence level Fails to reject the null "*": Significant at the 90% confidence level "**": Significant at the 95% confidence level "***": Significant at the 99% confidence level Notes ----- This class allows testing of joint hypothesis as long as all variables are continuous. References ---------- Racine, J.: "Consistent Significance Testing for Nonparametric Regression" Journal of Business & Economics Statistics. Chapter 12 in [1]. """ # Significance of continuous vars in nonparametric regression # Racine: Consistent Significance Testing for Nonparametric Regression # Journal of Business & Economics Statistics def __init__(self, model, test_vars, nboot=400, nested_res=400, pivot=False): self.nboot = nboot self.nres = nested_res self.test_vars = test_vars self.model = model self.bw = model.bw self.var_type = model.var_type self.k_vars = len(self.var_type) self.endog = model.endog self.exog = model.exog self.gx = model.est[model.reg_type] self.test_vars = test_vars self.pivot = pivot self.run() def run(self): self.test_stat = self._compute_test_stat(self.endog, self.exog) self.sig = self._compute_sig() def _compute_test_stat(self, Y, X): """ Computes the test statistic. See p.371 in [8]. """ lam = self._compute_lambda(Y, X) t = lam if self.pivot: se_lam = self._compute_se_lambda(Y, X) t = lam / float(se_lam) return t def _compute_lambda(self, Y, X): """Computes only lambda -- the main part of the test statistic""" n = np.shape(X)[0] Y = _adjust_shape(Y, 1) X = _adjust_shape(X, self.k_vars) b = KernelReg(Y, X, self.var_type, self.model.reg_type, self.bw, defaults = EstimatorSettings(efficient=False)).fit()[1] b = b[:, self.test_vars] b = np.reshape(b, (n, len(self.test_vars))) #fct = np.std(b) # Pivot the statistic by dividing by SE fct = 1. # Don't Pivot -- Bootstrapping works better if Pivot lam = ((b / fct) ** 2).sum() / float(n) return lam def _compute_se_lambda(self, Y, X): """ Calculates the SE of lambda by nested resampling Used to pivot the statistic. Bootstrapping works better with estimating pivotal statistics but slows down computation significantly. """ n = np.shape(Y)[0] lam = np.empty(shape=(self.nres, )) for i in range(self.nres): ind = np.random.randint(0, n, size=(n, 1)) Y1 = Y[ind, 0] X1 = X[ind, :] lam[i] = self._compute_lambda(Y1, X1) se_lambda = np.std(lam) return se_lambda def _compute_sig(self): """ Computes the significance value for the variable(s) tested. The empirical distribution of the test statistic is obtained through bootstrapping the sample. The null hypothesis is rejected if the test statistic is larger than the 90, 95, 99 percentiles. """ t_dist = np.empty(shape=(self.nboot, )) Y = self.endog X = copy.deepcopy(self.exog) n = np.shape(Y)[0] X[:, self.test_vars] = np.mean(X[:, self.test_vars], axis=0) # Calculate the restricted mean. See p. 372 in [8] M = KernelReg(Y, X, self.var_type, self.model.reg_type, self.bw, defaults = EstimatorSettings(efficient=False)).fit()[0] M = np.reshape(M, (n, 1)) e = Y - M e = e - np.mean(e) # recenter residuals for i in range(self.nboot): ind = np.random.randint(0, n, size=(n, 1)) e_boot = e[ind, 0] Y_boot = M + e_boot t_dist[i] = self._compute_test_stat(Y_boot, self.exog) self.t_dist = t_dist sig = "Not Significant" if self.test_stat > mquantiles(t_dist, 0.9): sig = "*" if self.test_stat > mquantiles(t_dist, 0.95): sig = "**" if self.test_stat > mquantiles(t_dist, 0.99): sig = "***" return sig class TestRegCoefD(TestRegCoefC): """ Significance test for the categorical variables in a nonparametric regression. Parameters ---------- model: Instance of KernelReg class This is the nonparametric regression model whose elements are tested for significance. test_vars: tuple, list of one element index of position of the discrete variable to be tested for significance. E.g. (3) tests variable at position 3 for significance. nboot: int Number of bootstrap samples used to determine the distribution of the test statistic in a finite sample. Default is 400 Attributes ---------- sig: str The significance level of the variable(s) tested "Not Significant": Not significant at the 90% confidence level Fails to reject the null "*": Significant at the 90% confidence level "**": Significant at the 95% confidence level "***": Significant at the 99% confidence level Notes ----- This class currently doesn't allow joint hypothesis. Only one variable can be tested at a time References ---------- See [9] and chapter 12 in [1]. """ def _compute_test_stat(self, Y, X): """Computes the test statistic""" dom_x = np.sort(np.unique(self.exog[:, self.test_vars])) n = np.shape(X)[0] model = KernelReg(Y, X, self.var_type, self.model.reg_type, self.bw, defaults = EstimatorSettings(efficient=False)) X1 = copy.deepcopy(X) X1[:, self.test_vars] = 0 m0 = model.fit(data_predict=X1)[0] m0 = np.reshape(m0, (n, 1)) I = np.zeros((n, 1)) for i in dom_x[1:] : X1[:, self.test_vars] = i m1 = model.fit(data_predict=X1)[0] m1 = np.reshape(m1, (n, 1)) I += (m1 - m0) ** 2 I = I.sum(axis=0) / float(n) return I def _compute_sig(self): """Calculates the significance level of the variable tested""" m = self._est_cond_mean() Y = self.endog X = self.exog n = np.shape(X)[0] u = Y - m u = u - np.mean(u) # center fct1 = (1 - 5**0.5) / 2. fct2 = (1 + 5**0.5) / 2. u1 = fct1 * u u2 = fct2 * u r = fct2 / (5 ** 0.5) I_dist = np.empty((self.nboot,1)) for j in range(self.nboot): u_boot = copy.deepcopy(u2) prob = np.random.uniform(0,1, size = (n,1)) ind = prob < r u_boot[ind] = u1[ind] Y_boot = m + u_boot I_dist[j] = self._compute_test_stat(Y_boot, X) sig = "Not Significant" if self.test_stat > mquantiles(I_dist, 0.9): sig = "*" if self.test_stat > mquantiles(I_dist, 0.95): sig = "**" if self.test_stat > mquantiles(I_dist, 0.99): sig = "***" return sig def _est_cond_mean(self): """ Calculates the expected conditional mean m(X, Z=l) for all possible l """ self.dom_x = np.sort(np.unique(self.exog[:, self.test_vars])) X = copy.deepcopy(self.exog) m=0 for i in self.dom_x: X[:, self.test_vars] = i m += self.model.fit(data_predict = X)[0] m = m / float(len(self.dom_x)) m = np.reshape(m, (np.shape(self.exog)[0], 1)) return m