statsmodels.tsa.arima_model.ARIMAResults class statsmodels.tsa.arima_model.ARIMAResults(model, params, normalized_cov_params=None, scale=1.0) [source] Methods aic() arfreq() Returns the frequency of the AR roots. arparams() arroots() bic() bse() conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params() f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. fittedvalues() forecast([steps, exog,

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fitstart NonlinearIVGMM.fitstart() [source]

statsmodels.sandbox.tsa.fftarma.ArmaFft.spdpoly ArmaFft.spdpoly(w, nma=50) [source] spectral density from MA polynomial representation for ARMA process References Cochrane, section 8.3.3

statsmodels.discrete.discrete_model.ProbitResults.wald_test ProbitResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A

statsmodels.genmod.families.links.nbinom.deriv nbinom.deriv(p) Derivative of the negative binomial transform Parameters: p : array-like Mean parameters Returns: g?(p) : array The derivative of the negative binomial transform link function Notes g?(x) = 1/(x+alpha*x^2)

statsmodels.discrete.discrete_model.BinaryResults.bic static BinaryResults.bic()

Autoregressive Moving Average (ARMA): Artificial data Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm import pandas as pd from statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) Generate some data from an ARMA process: In [2]: arparams = np.array([.75, -.25]) maparams = np.array([.65, .35]) The conventions of the arma_generate function require that we specify a 1 f

statsmodels.tsa.ar_model.ARResults.t_test ARResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple of arrays in the for

statsmodels.genmod.generalized_estimating_equations.GEEResults.wald_test GEEResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples.

Discrete Choice Models Link to Notebook GitHub Fair's Affair data A survey of women only was conducted in 1974 by Redbook asking about extramarital affairs. In [1]: from __future__ import print_function import numpy as np from scipy import stats import matplotlib.pyplot as plt import statsmodels.api as sm from statsmodels.formula.api import logit, probit, poisson, ols In [2]: print(sm.datasets.fair.SOURCE) Fair, Ray. 1978. "A Theory of Extramarital Affa