statsmodels.sandbox.regression.gmm.IV2SLS.from_formula classmethod IV2SLS.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : ex

statsmodels.tsa.vector_ar.irf.IRAnalysis.stderr IRAnalysis.stderr(orth=False) [source]

statsmodels.sandbox.regression.gmm.IV2SLS.fit IV2SLS.fit() [source] estimate model using 2SLS IV regression Returns: results : instance of RegressionResults regression result Notes This returns a generic RegressioResults instance as defined for the linear models. Parameter estimates and covariance are correct, but other results haven?t been tested yet, to seee whether they apply without changes.

statsmodels.tsa.vector_ar.irf.IRAnalysis.plot_cum_effects IRAnalysis.plot_cum_effects(orth=False, impulse=None, response=None, signif=0.05, plot_params=None, subplot_params=None, plot_stderr=True, stderr_type='asym', repl=1000, seed=None) Plot cumulative impulse response functions Parameters: orth : bool, default False Compute orthogonalized impulse responses impulse : string or int variable providing the impulse response : string or int variable affected by the impulse signif : float

statsmodels.tsa.vector_ar.irf.IRAnalysis.plot IRAnalysis.plot(orth=False, impulse=None, response=None, signif=0.05, plot_params=None, subplot_params=None, plot_stderr=True, stderr_type='asym', repl=1000, seed=None, component=None) Plot impulse responses Parameters: orth : bool, default False Compute orthogonalized impulse responses impulse : string or int variable providing the impulse response : string or int variable affected by the impulse signif : float (0 < signif < 1) Sig

statsmodels.tsa.vector_ar.irf.IRAnalysis.lr_effect_cov IRAnalysis.lr_effect_cov(orth=False) [source]

statsmodels.tsa.vector_ar.irf.IRAnalysis.lr_effect_stderr IRAnalysis.lr_effect_stderr(orth=False) [source]

statsmodels.tsa.vector_ar.irf.IRAnalysis.fevd_table IRAnalysis.fevd_table() [source]

statsmodels.tsa.vector_ar.irf.IRAnalysis.err_band_sz2 IRAnalysis.err_band_sz2(orth=False, repl=1000, signif=0.05, seed=None, burn=100, component=None) [source] IRF Sims-Zha error band method 2. This method Does not assume symmetric error bands around mean. Parameters: orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : in

statsmodels.tsa.vector_ar.irf.IRAnalysis.err_band_sz3 IRAnalysis.err_band_sz3(orth=False, repl=1000, signif=0.05, seed=None, burn=100, component=None) [source] IRF Sims-Zha error band method 3. Does not assume symmetric error bands around mean. Parameters: orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, default N