statsmodels.discrete.discrete_model.Poisson.initialize Poisson.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

statsmodels.iolib.table.SimpleTable.count SimpleTable.count(value) ? integer -- return number of occurrences of value

statsmodels.genmod.families.links.Power.deriv2 Power.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.tsa.vector_ar.var_model.FEVD.plot FEVD.plot(periods=None, figsize=(10, 10), **plot_kwds) [source] Plot graphical display of FEVD Parameters: periods : int, default None Defaults to number originally specified. Can be at most that number

statsmodels.sandbox.regression.gmm.IVGMMResults.jtest IVGMMResults.jtest() overidentification test I guess this is missing a division by nobs, what?s the normalization in jval ?

Prediction (out of sample) Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm Artificial data In [2]: nsample = 50 sig = 0.25 x1 = np.linspace(0, 20, nsample) X = np.column_stack((x1, np.sin(x1), (x1-5)**2)) X = sm.add_constant(X) beta = [5., 0.5, 0.5, -0.02] y_true = np.dot(X, beta) y = y_true + sig * np.random.normal(size=nsample) Estimation In [3]: olsmod = sm.OLS(y, X) olsres = olsmo

statsmodels.sandbox.regression.gmm.IVRegressionResults.mse_model static IVRegressionResults.mse_model()

statsmodels.regression.linear_model.OLSResults.cov_HC0 static OLSResults.cov_HC0() See statsmodels.RegressionResults

statsmodels.discrete.discrete_model.DiscreteModel.loglike DiscreteModel.loglike(params) Log-likelihood of model.

statsmodels.sandbox.regression.gmm.GMMResults.jtest GMMResults.jtest() [source] overidentification test I guess this is missing a division by nobs, what?s the normalization in jval ?