statsmodels.discrete.discrete_model.BinaryResults.conf_int BinaryResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interva

statsmodels.tsa.arima_model.ARIMAResults.summary2 ARIMAResults.summary2(title=None, alpha=0.05, float_format='%.4f') Experimental summary function for ARIMA Results Parameters: title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format: string : print format for floats in parameters summary Returns: smry : Summary instance This holds the summary table and text, which

Discrete Choice Models Overview Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm Data Load data from Spector and Mazzeo (1980). Examples follow Greene's Econometric Analysis Ch. 21 (5th Edition). In [2]: spector_data = sm.datasets.spector.load() spector_data.exog = sm.add_constant(spector_data.exog, prepend=False) Inspect the data: In [3]: print(spector_data.exog[:5,:]) print(spector_d

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.coefs static DynamicVAR.coefs() [source] Return dynamic regression coefficients as WidePanel

statsmodels.discrete.discrete_model.LogitResults.cov_params LogitResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like,

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.fit_loc_scale ExpTransf_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.regression.mixed_linear_model.MixedLMResults.pvalues static MixedLMResults.pvalues()

statsmodels.tsa.ar_model.AR.hessian AR.hessian(params) [source] Returns numerical hessian for now.

statsmodels.genmod.families.links.inverse_squared class statsmodels.genmod.families.links.inverse_squared [source] The inverse squared transform Notes g(p) = 1/(p**2) Alias of statsmodels.family.links.Power(power=2.) Methods deriv(p) Derivative of the power transform deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of the power transform link function inverse_deriv(z) Derivative of the inverse of the power transform

statsmodels.sandbox.tsa.fftarma.ArmaFft.pacf ArmaFft.pacf(nobs=None) partial autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned pacf Returns: pacf : array partial autocorrelation of ARMA process given by ar, ma Notes solves yule-walker