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连老师，stata 中sVAR模型，残差的方差和协方差保存在下面哪个返回值中啊

另外，如何在stata中实现VAR模型的方差分析呢，视频中好象没有讲到这一点，

再有就是在sVAR估计时，如果个别系数不显著，分析起来还有意义么，在其他计量经济模型中呢

是不是通过适当选取变量滞后阶数，一定可以找到使系数显著的滞后阶数（滞后阶数一定要根据信息准则来确定么）

谢谢连老师

svar saves the following in e():

Scalars
e(N) number of observations
e(k_eq) number of equations
e(k_eq_var) number of equations in underlying VAR
e(k_dv) number of dependent variables
e(k_var) number of coefficients in VAR
e(k_aux) number of auxiliary parameters
e(mlag_var) highest lag in VAR
e(k_dv_var) number of dependent variables in underlying VAR
e(tparms_var) number of parameters in all equations
e(df_eq_var) average number of parameters in an equation
e(df_m_var) model degrees of freedom
e(df_r_var) if small, residual degrees of freedom
e(obs_#_var) number of observations on equation #
e(k_#_var) number of coefficients in equation #
e(df_m#_var) model degrees of freedom for equation #
e(r2_#_var) R-squared for equation #
e(chi2_#_var) chi-squared statistic for equation #
e(rmse_#_var) root mean squared error for equation #
e(ll_#) log likelihood for equation #
e(df_r#_var) residual degrees of freedom for equation # (small only)
e(F_#_var) F statistic for equation # (small only)
e(N_gaps_var) number of gaps in the sample
e(detsig_ml_var) determinant of Sigma_ml hat
e(aic_var) Akaike information criterion
e(sbic_var) Schwarz-Bayesian information criterion
e(hqic_var) Hannan-Quinn information criterion
e(fpe_var) final prediction error
e(ll) log likelihood from svar
e(ll_var) log likelihood from var
e(ll_#_var) log likelihood for equation # VAR
e(tmin) first time period in the sample
e(tmax) maximum time
e(detsig_var) determinant of e(Sigma)
e(chi2_oid) overidentification test
e(oid_df) number of overidentifying restrictions
e(ic_ml) number of iterations
e(rc_ml) return code from ml
e(N_cns) number of constraints

Macros
e(cmd) svar
e(cmdline) command as typed
e(lrmodel) long-run model, if specified
e(lags_var) lags in model
e(depvar_var) names of dependent variables
e(endog_var) names of endogenous variables
e(exog_var) names of exogenous variables, if specified
e(nocons_var) noconstant, if noconstant specified
e(cns_lr) long-run constraints
e(cns_a) cross-parameter equality constraints on A
e(cns_b) cross-parameter equality constraints on B
e(dfk_var) alternate divisor (dfk), if specified
e(eqnames_var) names of equations
e(lutstats_var) lutstats, if specified
e(constraints_var) constraints_var, if there are constraints on VAR
e(small) small, if specified
e(tsfmt) format of timevar
e(timevar) name of timevar
e(title) title in estimation output
e(properties) b V
e(predict) program used to implement predict

Matrices
e(b) coefficient vector
e(V) variance-covariance matrix of the estimators
e(b_var) coefficient vector of underlying VAR model
e(V_var) VCE of underlying VAR model
e(bf_var) full coefficient vector with zeros in dropped lags
e(G_var) Gamma matrix saved by var; see Methods and Formulas in [TS] var svar
e(Sigma) Sigma hat matrix
e(aeq) aeq(matrix), if specified
e(acns) acns(matrix), if specified
e(beq) beq(matrix), if specified
e(bcns) bcns(matrix), if specified
e(lreq) lreq(matrix), if specified
e(lrcns) lrcns(matrix), if specified
e(Cns_var) constraint matrix from var, if varconstraints() is specified
e(A) estimated A matrix, if a short-run model
e(B) estimated B matrix
e(C) estimated C matrix, if a long-run model
e(A1) estimated A bar matrix, if a long-run model

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