stata 用outreg2输出回归结果 | DataLearnerAI

stata 用outreg2输出回归结果 | DataLearnerAI

Scalars        
  e(N)                number of observations
  e(N_g)              number of groups
  e(df_m)             model degrees of freedom
  e(g_min)            smallest group size
  e(g_avg)            average group size
  e(g_max)            largest group size
  e(Tcon)             1 if T is constant
  e(sigma)            ancillary parameter (gamma, lnormal)
  e(sigma_u)          panel-level standard deviation
  e(sigma_e)          standard deviation of epsilon_it
  e(r2_w)             R-squared within model
  e(r2_o)             R-squared overall model
  e(r2_b)             R-squared between model
  e(N_clust)          number of clusters
  e(chi2)             chi-squared
  e(p)                significance
  e(rho)              rho
  e(thta_min)         minimum theta
  e(thta_5)           theta, 5th percentile
  e(thta_50)          theta, 50th percentile
  e(thta_95)          theta, 95th percentile
  e(thta_max)         maximum theta
  e(rmse)             root mean squared error of GLS regression
  e(Tbar)             harmonic mean of group sizes
  e(rank)             rank of e(V)

Macros         
  e(cmd)              xtreg
  e(cmdline)          command as typed
  e(depvar)           name of dependent variable
  e(ivar)             variable denoting groups
  e(model)            re
  e(clustvar)         name of cluster variable
  e(chi2type)         Wald; type of model chi-squared test
  e(vce)              vcetype specified in vce()
  e(vcetype)          title used to label Std. Err.
  e(sa)               Swamy-Arora estimator of the variance components (sa
                        only)
  e(properties)       b V
  e(predict)          program used to implement predict
  e(marginsnotok)     predictions disallowed by margins
  e(asbalanced)       factor variables fvset as asbalanced
  e(asobserved)       factor variables fvset as asobserved

Matrices       
  e(b)                coefficient vector
  e(bf)               coefficient vector for fixed-effects model
  e(theta)            theta
  e(V)                variance-covariance matrix of the estimators
  e(VCEf)             VCE for fixed-effects model

Functions      
  e(sample)           marks estimation sample


xtreg, be stores the following in e():

Scalars        
  e(N)                number of observations
  e(N_g)              number of groups
  e(typ)              WLS, if wls specified
  e(mss)              model sum of squares
  e(df_m)             model degrees of freedom
  e(rss)              residual sum of squares
  e(df_r)             residual degrees of freedom
  e(ll)               log likelihood
  e(ll_0)             log likelihood, constant-only model
  e(g_min)            smallest group size
  e(g_avg)            average group size
  e(g_max)            largest group size
  e(Tcon)             1 if T is constant
  e(r2)               R-squared
  e(r2_a)             adjusted R-squared
  e(r2_w)             R-squared within model
  e(r2_o)             R-squared overall model
  e(r2_b)             R-squared between model
  e(F)                F statistic
  e(rmse)             root mean squared error
  e(Tbar)             harmonic mean of group sizes
  e(rank)             rank of e(V)

Macros         
  e(cmd)              xtreg
  e(cmdline)          command as typed
  e(depvar)           name of dependent variable
  e(ivar)             variable denoting groups
  e(model)            be
  e(title)            title in estimation output
  e(vce)              vcetype specified in vce()
  e(vcetype)          title used to label Std. Err.
  e(properties)       b V
  e(predict)          program used to implement predict
  e(marginsok)        predictions allowed by margins
  e(marginsnotok)     predictions disallowed by margins
  e(asbalanced)       factor variables fvset as asbalanced
  e(asobserved)       factor variables fvset as asobserved

Matrices       
  e(b)                coefficient vector
  e(V)                variance-covariance matrix of the estimators

Functions      
  e(sample)           marks estimation sample


xtreg, fe stores the following in e():

Scalars        
  e(N)                number of observations
  e(N_g)              number of groups
  e(mss)              model sum of squares
  e(df_m)             model degrees of freedom
  e(rss)              residual sum of squares
  e(df_r)             residual degrees of freedom
  e(tss)              total sum of squares
  e(g_min)            smallest group size
  e(g_avg)            average group size
  e(g_max)            largest group size
  e(Tcon)             1 if T is constant
  e(sigma)            ancillary parameter (gamma, lnormal)
  e(corr)             corr(u_i, Xb)
  e(sigma_u)          panel-level standard deviation
  e(sigma_e)          standard deviation of epsilon_it
  e(r2)               R-squared
  e(r2_a)             adjusted R-squared
  e(r2_w)             R-squared within model
  e(r2_o)             R-squared overall model
  e(r2_b)             R-squared between model
  e(ll)               log likelihood
  e(ll_0)             log likelihood, constant-only model
  e(N_clust)          number of clusters
  e(rho)              rho
  e(F)                F statistic
  e(F_f)              F for u_i=0
  e(df_a)             degrees of freedom for absorbed effect
  e(df_b)             numerator degrees of freedom for F statistic
  e(rmse)             root mean squared error
  e(Tbar)             harmonic mean of group sizes
  e(rank)             rank of e(V)

Macros         
  e(cmd)              xtreg
  e(cmdline)          command as typed
  e(depvar)           name of dependent variable
  e(ivar)             variable denoting groups
  e(model)            fe
  e(wtype)            weight type
  e(wexp)             weight expression
  e(clustvar)         name of cluster variable
  e(vce)              vcetype specified in vce()
  e(vcetype)          title used to label Std. Err.
  e(properties)       b V
  e(predict)          program used to implement predict
  e(marginsnotok)     predictions disallowed by margins
  e(asbalanced)       factor variables fvset as asbalanced
  e(asobserved)       factor variables fvset as asobserved

Matrices       
  e(b)                coefficient vector
  e(Cns)              constraints matrix
  e(V)                variance-covariance matrix of the estimators
  e(V_modelbased)     model-based variance

Functions      
  e(sample)           marks estimation sample