statsmodels.stats.correlation_tools.cov_nearest statsmodels.stats.correlation_tools.cov_nearest(cov, method='clipped', threshold=1e-15, n_fact=100, return_all=False) [source] Find the nearest covariance matrix that is postive (semi-) definite This leaves the diagonal, i.e. the variance, unchanged Parameters: cov : ndarray, (k,k) initial covariance matrix method : string if ?clipped?, then the faster but less accurate corr_clipped is used. if ?nearest?, then corr_nearest is used threshol

statsmodels.stats.correlation_tools.corr_nearest statsmodels.stats.correlation_tools.corr_nearest(corr, threshold=1e-15, n_fact=100) [source] Find the nearest correlation matrix that is positive semi-definite. The function iteratively adjust the correlation matrix by clipping the eigenvalues of a difference matrix. The diagonal elements are set to one. Parameters: corr : ndarray, (k, k) initial correlation matrix threshold : float clipping threshold for smallest eigenvalue, see Notes n_

statsmodels.stats.correlation_tools.corr_clipped statsmodels.stats.correlation_tools.corr_clipped(corr, threshold=1e-15) [source] Find a near correlation matrix that is positive semi-definite This function clips the eigenvalues, replacing eigenvalues smaller than the threshold by the threshold. The new matrix is normalized, so that the diagonal elements are one. Compared to corr_nearest, the distance between the original correlation matrix and the positive definite correlation matrix is larg

statsmodels.stats.anova.anova_lm statsmodels.stats.anova.anova_lm(*args, **kwargs) [source] ANOVA table for one or more fitted linear models. Parameters: args : fitted linear model results instance One or more fitted linear models scale : float Estimate of variance, If None, will be estimated from the largest model. Default is None. test : str {?F?, ?Chisq?, ?Cp?} or None Test statistics to provide. Default is ?F?. typ : str or int {?I?,?II?,?III?} or {1,2,3} The type of ANOVA test t

Statistics stats This section collects various statistical tests and tools. Some can be used independently of any models, some are intended as extension to the models and model results. API Warning: The functions and objects in this category are spread out in various modules and might still be moved around. We expect that in future the statistical tests will return class instances with more informative reporting instead of only the raw numbers. Residual Diagnostics and Specification Tests dur

statsmodels.tsa.vector_ar.var_model.VARResults.tvalues static VARResults.tvalues() [source] Compute t-statistics. Use Student-t(T - Kp - 1) = t(df_resid) to test significance.

statsmodels.tsa.vector_ar.var_model.VARResults.stderr static VARResults.stderr() [source] Standard errors of coefficients, reshaped to match in size

statsmodels.tsa.vector_ar.var_model.VARResults.sigma_u_mle static VARResults.sigma_u_mle() [source] (Biased) maximum likelihood estimate of noise process covariance

statsmodels.tsa.vector_ar.var_model.VARResults.roots static VARResults.roots() [source]

statsmodels.tsa.vector_ar.var_model.VARResults.resid_corr static VARResults.resid_corr() [source] Centered residual correlation matrix