statsmodels.tsa.vector_ar.var_model.VARResults.plot_acorr VARResults.plot_acorr(nlags=10, linewidth=8) Plot theoretical autocorrelation function

statsmodels.sandbox.tsa.movstat.movmean statsmodels.sandbox.tsa.movstat.movmean(x, windowsize=3, lag='lagged') [source] moving window mean Parameters: x : array time series data windsize : int window size lag : ?lagged?, ?centered?, or ?leading? location of window relative to current position Returns: mk : array moving mean, with same shape as x Notes for leading and lagging the data array x is extended by the closest value of the array

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.regression.gmm.IVGMMResults.f_test IVGMMResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. 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

statsmodels.sandbox.regression.gmm.IVGMM.predict IVGMM.predict(params, exog=None) [source]

statsmodels.sandbox.regression.gmm.IVGMMResults.cov_params IVGMMResults.cov_params(**kwds)

statsmodels.regression.linear_model.OLSResults.fvalue static OLSResults.fvalue()

statsmodels.sandbox.distributions.transformed.loggammaexpg statsmodels.sandbox.distributions.transformed.loggammaexpg = univariate distribution of a non-linear monotonic transformation of a random variable

statsmodels.tsa.arima_process.ArmaProcess.periodogram ArmaProcess.periodogram(nobs=None) [source] periodogram for ARMA process given by lag-polynomials ar and ma Parameters: ar : array_like autoregressive lag-polynomial with leading 1 and lhs sign ma : array_like moving average lag-polynomial with leading 1 worN : {None, int}, optional option for scipy.signal.freqz (read ?w or N?) If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that

statsmodels.regression.linear_model.OLSResults.cov_HC3 static OLSResults.cov_HC3() See statsmodels.RegressionResults