statsmodels.tsa.vector_ar.irf.IRAnalysis.errband_mc IRAnalysis.errband_mc(orth=False, svar=False, repl=1000, signif=0.05, seed=None, burn=100) [source] IRF Monte Carlo integrated error bands

statsmodels.tsa.vector_ar.irf.IRAnalysis.cum_errband_mc IRAnalysis.cum_errband_mc(orth=False, repl=1000, signif=0.05, seed=None, burn=100) [source] IRF Monte Carlo integrated error bands of cumulative effect

statsmodels.tsa.vector_ar.irf.IRAnalysis.err_band_sz1 IRAnalysis.err_band_sz1(orth=False, svar=False, repl=1000, signif=0.05, seed=None, burn=100, component=None) [source] IRF Sims-Zha error band method 1. Assumes 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, defau

statsmodels.tsa.vector_ar.irf.IRAnalysis.cum_effect_cov IRAnalysis.cum_effect_cov(orth=False) [source] Compute asymptotic standard errors for cumulative impulse response coefficients Parameters: orth : boolean Notes eq. 3.7.7 (non-orth), 3.7.10 (orth)

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

statsmodels.iolib.table.SimpleTable class statsmodels.iolib.table.SimpleTable(data, headers=None, stubs=None, title='', datatypes=None, csv_fmt=None, txt_fmt=None, ltx_fmt=None, html_fmt=None, celltype=None, rowtype=None, **fmt_dict) [source] Produce a simple ASCII, CSV, HTML, or LaTeX table from a rectangular (2d!) array of data, not necessarily numerical. Directly supports at most one header row, which should be the length of data[0]. Directly supports at most one stubs column, which must

statsmodels.tsa.vector_ar.irf.IRAnalysis.cov IRAnalysis.cov(orth=False) [source] Compute asymptotic standard errors for impulse response coefficients Notes Lutkepohl eq 3.7.5

statsmodels.iolib.table.csv2st statsmodels.iolib.table.csv2st(csvfile, headers=False, stubs=False, title=None) [source] Return SimpleTable instance, created from the data in csvfile, which is in comma separated values format. The first row may contain headers: set headers=True. The first column may contain stubs: set stubs=True. Can also supply headers and stubs as tuples of strings.

statsmodels.iolib.summary2.Summary class statsmodels.iolib.summary2.Summary [source] Methods add_array(array[, align, float_format]) Add the contents of a Numpy array to summary table add_base(results[, alpha, float_format, ...]) Try to construct a basic summary instance. add_df(df[, index, header, float_format, align]) Add the contents of a DataFrame to summary table add_dict(d[, ncols, align, float_format]) Add the contents of a Dict to summary table add_text(string) Append a note to

statsmodels.iolib.smpickle.save_pickle statsmodels.iolib.smpickle.save_pickle(obj, fname) [source] Save the object to file via pickling. Parameters: fname : str Filename to pickle to