statsmodels.tsa.vector_ar.var_model.VARResults.irf_resim VARResults.irf_resim(orth=False, repl=1000, T=10, seed=None, burn=100, cum=False) [source] Simulates impulse response function, returning an array of simulations. Used for Sims-Zha error band calculation. Parameters: orth: bool, default False : Compute orthoganalized impulse response error bands repl: int : number of Monte Carlo replications to perform T: int, default 10 : number of impulse response periods signif: float (0 <

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.nnlf SkewNorm2_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.tsa.ar_model.ARResults.pvalues static ARResults.pvalues() [source]

statsmodels.discrete.discrete_model.NegativeBinomialResults.bse static NegativeBinomialResults.bse()

Import Paths and Structure We offer two ways of importing functions and classes from statsmodels: API import for interactive useAllows tab completion Direct import for programsAvoids importing unnecessary modules and commands API Import for interactive use For interactive use the recommended import is: import statsmodels.api as sm Importing statsmodels.api will load most of the public parts of statsmodels. This makes most functions and classes conveniently available within one or two lev

statsmodels.tsa.filters.bk_filter.bkfilter statsmodels.tsa.filters.bk_filter.bkfilter(X, low=6, high=32, K=12) [source] Baxter-King bandpass filter Parameters: X : array-like A 1 or 2d ndarray. If 2d, variables are assumed to be in columns. low : float Minimum period for oscillations, ie., Baxter and King suggest that the Burns-Mitchell U.S. business cycle has 6 for quarterly data and 1.5 for annual data. high : float Maximum period for oscillations BK suggest that the U.S. business cy

statsmodels.sandbox.distributions.extras.NormExpan_gen.fit_loc_scale NormExpan_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.discrete.discrete_model.CountModel.loglike CountModel.loglike(params) Log-likelihood of model.

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

statsmodels.tsa.arima_process.ArmaProcess.arma2ma ArmaProcess.arma2ma(nobs=None) [source]