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我的数据是上市公司面板数据，id为公司，year为年度，ind为行业，我进行了 tsset id year的设定。
根据我所有研究的主题及数据的情况，经过分析，已经确定使用静态面板随机效应模型。（xtreg y x，re）
根据理论及数据的情况，为了得到稳健的标准误，我首先在id上进行cluster，stata报告了该模型的chi2和其相应的p值、r2_w；但我在ind上进行cluster后，stata报告了r2_w，没有报告该模型的chi2和其相应的p值。
我的问题是：
（1）在行业（indu）上群聚后为什么没有报告chi2和其相应的p值？
（2）我想要行业（indu）上群聚后的报告结果，因为这个结果与我前面的理论分析与预期非常吻合与一致。在我的论文中chi2和其相应的p值应该如何报告呢？是省略？还是报告id上进行cluster后的chi2和其相应的p值，而同时标准误使用ind上进行cluster时的标准误呢？
盼望您的回复！
载入中......
善待你一生！
让网络基于真人的故事！
由于没有看到具体数据，我也不太确定问题出在哪里。不过，论文中很少报告 chi2 值。
arlionn 发表于
由于没有看到具体数据，我也不太确定问题出在哪里。不过，论文中很少报告 chi2 值。连老师，您帮我看看.
. xtreg&&w_rdgs_dp&&w_tfp_lp pc actdum fb $cona $area $indu $year, re robust cluster(id)
Random-effects GLS regression& && && && && && & Number of obs& && &=& && &1450
Group variable: id& && && && && && && && && && &Number of groups& &=& && & 529
R-sq:&&within&&= 0.0226& && && && && && && && & Obs per group: min =& && && &1
between = 0.1211& && && && && && && && && && && && && & avg =& && & 2.7
overall = 0.0658& && && && && && && && && && && && && & max =& && && &7
Wald chi2(31)& && &=& &&&64.00
corr(u_i, X)& &= 0 (assumed)& && && && && && &&&Prob & chi2& && &&&=& & 0.0004
(Std. Err. adjusted for 529 clusters in id)
w_rdgs_dp& && & Coef.& &Std. Err.& && &z& & P&|z|& &&&[95% Conf. Interval]
w_tfp_lp& &-5.661512& &1.647925& & -3.44& &0.001& & -8.891385& &-2.431639
pc& & 1.647489& &1.414227& &&&1.16& &0.244& & -1.124344& & 4.419322
actdum& & 4.156381& &1.641691& &&&2.53& &0.011& &&&.9387267& & 7.374036
fb& & 2.964916& &1.775937& &&&1.67& &0.095& & -.5158571& &&&6.44569
w_lev& & 9.794413& &5.318462& &&&1.84& &0.066& & -.6295813& & 20.21841
w_growth& &-3.931117& &2.137813& & -1.84& &0.066& & -8.121153& & .2589182
w_manage& &-8.273467& &3.636864& & -2.27& &0.023& & -15.40159& &-1.145345
w_capital_as& & 2.911468& &1.074252& &&&2.71& &0.007& && &.805972& & 5.016964
w_employa& &-.0976049& &.8091332& & -0.12& &0.904& & -1.683477& & 1.488267
w_lnasset& &-2.197348& &.8848904& & -2.48& &0.013& & -3.931701& &-.4629943
w_lnage& &-.3726409& &1.836595& & -0.20& &0.839& & -3.972301& & 3.227019
w_h3& &-12.35768& &5.672104& & -2.18& &0.029& &&&-23.4748& &-1.240558
area_dum2& & 2.083866& &3.160761& &&&0.66& &0.510& & -4.111112& & 8.278843
area_dum3& & .7387732& &3.378895& &&&0.22& &0.827& &&&-5.88374& & 7.361286
area_dum4& &-1.059868& &4.748704& & -0.22& &0.823& & -10.36716& &&&8.24742
area_dum5& & 4.036306& &4.169152& &&&0.97& &0.333& & -4.135082& & 12.20769
area_dum6& &-2.275075& &3.057881& & -0.74& &0.457& & -8.268412& & 3.718262
indu_dum2& & 2.540734& &5.428724& &&&0.47& &0.640& & -8.099369& & 13.18084
indu_dum3& &-1.418513& &2.746518& & -0.52& &0.606& & -6.801589& & 3.964563
indu_dum4& & 2.228628& &4.190515& &&&0.53& &0.595& &&&-5.98463& & 10.44189
indu_dum5& &-6.813475& &2.358388& & -2.89& &0.004& & -11.43583& &-2.191119
indu_dum6& &-4.650413& &2.959087& & -1.57& &0.116& & -10.45012& &&&1.14929
indu_dum7& &-3.093613& &2.891101& & -1.07& &0.285& & -8.760066& &&&2.57284
indu_dum8& &-4.058192& &2.736872& & -1.48& &0.138& & -9.422362& & 1.305978
indu_dum9& &-8.954838& &3.255874& & -2.75& &0.006& & -15.33623& &-2.573441
year_dum2& &-.6660892& &1.908675& & -0.35& &0.727& & -4.407023& & 3.074844
year_dum3& & 1.455915& &2.113208& &&&0.69& &0.491& & -2.685896& & 5.597725
year_dum4& & .4397084& &1.734909& &&&0.25& &0.800& &&&-2.96065& & 3.840067
year_dum5& & 7.000571& &2.663299& &&&2.63& &0.009& &&&1.780601& & 12.22054
year_dum6& & 7.192509& &2.574446& &&&2.79& &0.005& &&&2.146688& & 12.23833
year_dum7& & 7.728862& &2.629039& &&&2.94& &0.003& && &2.57604& & 12.88168
_cons& &&&33.0568& &11.23186& &&&2.94& &0.003& &&&11.04276& & 55.07084
sigma_u& &7.3859076
sigma_e& &24.393637
rho& &.& &(fraction of variance due to u_i)
xtreg&&w_rdgs_dp&&w_tfp_lp pc actdum fb $cona $area $indu $year, re robust cluster(indu)
Random-effects GLS regression& && && && && && & Number of obs& && &=& && &1450
Group variable: id& && && && && && && && && && &Number of groups& &=& && & 529
R-sq:&&within&&= 0.0226& && && && && && && && & Obs per group: min =& && && &1
between = 0.1211& && && && && && && && && && && && && & avg =& && & 2.7
overall = 0.0658& && && && && && && && && && && && && & max =& && && &7
Wald chi2(8)& && & =& && && &.
corr(u_i, X)& &= 0 (assumed)& && && && && && &&&Prob & chi2& && &&&=& && && &.
(Std. Err. adjusted for 9 clusters in indu)
w_rdgs_dp& && & Coef.& &Std. Err.& && &z& & P&|z|& &&&[95% Conf. Interval]
w_tfp_lp& &-5.661512& &1.723884& & -3.28& &0.001& & -9.040262& &-2.282761
pc& & 1.647489& &.6448088& &&&2.56& &0.011& &&&.3836872& & 2.911291
actdum& & 4.156381& &1.328166& &&&3.13& &0.002& &&&1.553224& & 6.759538
fb& & 2.964916& &1.069011& &&&2.77& &0.006& &&&.8696938& & 5.060139
w_lev& & 9.794413& & 4.57453& &&&2.14& &0.032& &&&.8284994& & 18.76033
w_growth& &-3.931117& &2.516738& & -1.56& &0.118& & -8.863833& & 1.001599
w_manage& &-8.273467& &4.843094& & -1.71& &0.088& & -17.76576& & 1.218823
w_capital_as& & 2.911468& &.9536258& &&&3.05& &0.002& &&&1.042396& &&&4.78054
w_employa& &-.0976049& &.7697532& & -0.13& &0.899& & -1.606293& & 1.411084
w_lnasset& &-2.197348& &.6726968& & -3.27& &0.001& & -3.515809& &-.8788862
w_lnage& &-.3726409& &1.595079& & -0.23& &0.815& & -3.498938& & 2.753656
w_h3& &-12.35768& &6.210609& & -1.99& &0.047& & -24.53025& &-.1851077
area_dum2& & 2.083866& &2.896726& &&&0.72& &0.472& & -3.593614& & 7.761345
area_dum3& & .7387732& &2.459155& &&&0.30& &0.764& & -4.081081& & 5.558627
area_dum4& &-1.059868& &2.802059& & -0.38& &0.705& & -6.551804& & 4.432067
area_dum5& & 4.036306& &2.996548& &&&1.35& &0.178& & -1.836819& & 9.909432
area_dum6& &-2.275075& &2.832111& & -0.80& &0.422& &&&-7.82591& & 3.275759
indu_dum2& & 2.540734& &.8382171& &&&3.03& &0.002& &&&.8978582& & 4.183609
indu_dum3& &-1.418513& &.4362996& & -3.25& &0.001& & -2.273645& &-.5633818
indu_dum4& & 2.228628& &.8129474& &&&2.74& &0.006& &&&.6352802& & 3.821975
indu_dum5& &-6.813475& &.9728887& & -7.00& &0.000& & -8.720302& &-4.906649
indu_dum6& &-4.650413& &.5416697& & -8.59& &0.000& & -5.712066& & -3.58876
indu_dum7& &-3.093613& &.8623051& & -3.59& &0.000& && &-4.7837& &-1.403526
indu_dum8& &-4.058192& &.4320661& & -9.39& &0.000& & -4.905026& &-3.211358
indu_dum9& &-8.954838& &.9538219& & -9.39& &0.000& & -10.82429& &-7.085381
year_dum2& &-.6660892& &2.223743& & -0.30& &0.765& & -5.024545& & 3.692367
year_dum3& & 1.455915& &3.255438& &&&0.45& &0.655& & -4.924626& & 7.836456
year_dum4& & .4397084& &2.040907& &&&0.22& &0.829& & -3.560395& & 4.439812
year_dum5& & 7.000571& & 4.42658& &&&1.58& &0.114& & -1.675366& & 15.67651
year_dum6& & 7.192509& &&&1.1419& &&&6.30& &0.000& &&&4.954427& & 9.430591
year_dum7& & 7.728862& &3.990767& &&&1.94& &0.053& & -.0928981& & 15.55062
_cons& &&&33.0568& &12.67461& &&&2.61& &0.009& &&&8.215025& & 57.89857
sigma_u& &7.3859076
sigma_e& &24.393637
rho& &.& &(fraction of variance due to u_i)
善待你一生！
让网络基于真人的故事！
arlionn 发表于
由于没有看到具体数据，我也不太确定问题出在哪里。不过，论文中很少报告 chi2 值。连老师，用xtreg，re模型时，stata报告的是chi2值啊，为什么没有报告F值。
善待你一生！
让网络基于真人的故事！
去掉 robust 选项试一下。
连老师，去掉 robust选项后，仍然没有报告chi2值。
是不是stata在RE模型中不报告F统计量呢？
善待你一生！
让网络基于真人的故事！
arlionn 发表于
去掉 robust 选项试一下。连老师，去掉 robust选项后，仍然没有报告chi2值。
是不是stata在RE模型中不报告F统计量呢？
善待你一生！
让网络基于真人的故事！
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Documentation for estadd
help estadd
also see: , , ,
http://repec.org/bocode/e/estout
-------------------------------------------------------------------------------
estadd -- Add results to (stored) estimates
] [ : namelist ]
where namelist is _all | * | name [name ...]
subcommands
description
-----------------------------------------------------------------
Elementary
add a macro
name = exp
add a scalar
name = mat
add a matrix
add contents of r(name) (matrix or scalar)
Statistics for each
coefficient
standardized coefficients
variance inflation factors (after regress)
partial (and semi-partial) correlations
exponentiated coefficients
standardized factor change coefficients
means of regressors
standard deviations of regressors
various descriptives of the regressors
Summary statistics
Cox & Snell's pseudo R-squared
Nagelkerke's pseudo R-squared
likelihood-ratio test
descriptives of the dependent variable
add results from margins (Stata 11 or newer)
add results from brant (if installed)
add results from fitstat (if installed)
add results from listcoef (if installed)
add results from mlogtest (if installed)
add results from prchange (if installed)
add results from prvalue (if installed)
add results from asprvalue (if installed)
-----------------------------------------------------------------
description
-----------------------------------------------------------------
permit overwriting existing e()'s
prefix(string)
specify prefix for names of added results
suppress output from subcommand (if any)
subcmdopts
subcommand specific options
-----------------------------------------------------------------
Description
estadd adds additional results to the e()-returns of an estimation
command (see help , help ). If no namelist is provided, then
the results are added to the currently active estimates (i.e. the model
fit last). If these estimates have been previously stored, the stored
copy of the estimates will also be modified. Alternatively, if namelist
is provided after the colon, results are added to all indicated sets of
stored estimates (see help
or help ). You may use
the * and ?
wildcards in namelist. Execution is silent if namelist is
Adding additional results to the e()-returns is useful, for example, if
the estimates be tabulated by commands such as
or . See the
section below for illustration of the usage of estadd.
Technical note: Some of the subcommands below make use of the information
contained in e(sample) to determine estimation sample.
These subcommands
return error if the estimates do not contain e(sample).
Subcommands
+------------+
----+ Elementary +-------------------------------------------------------
estadd local name ...
adds in macro e(name) the specified contents (also see help ).
estadd scalar name = exp
adds in scalar e(name) the evaluation of exp (also see help ).
name must not be b or V.
estadd scalar r(name)
adds in scalar e(name) the value of scalar r(name). name must not be
estadd scalar name
adds in scalar e(name) the the value of scalar name. name must not be
estadd matrix name = matrix_expression
adds in matrix e(name) the evaluation of matrix_expression (also see
help ). name must not be b or V.
estadd matrix r(name)
adds in matrix e(name) a copy of matrix r(name). name must not be b
estadd matrix name
adds in matrix e(name) a copy of matrix name. name must not be b or
estadd r(name)
adds in e(name) the value of scalar r(name) or a copy of matrix
r(name), depending on the nature of r(name). name must not be b or V.
+---------------------------------+
----+ Statistics for each coefficient +----------------------------------
estadd beta
adds in e(beta) the standardized beta coefficients.
estadd vif [, tolerance sqrvif ]
adds in e(vif) the variance inflation factors (VIFs) for the
regressors (see help ). Note that vif only works with estimates
produced by . tolerance additionally adds the tolerances
(1/VIF) in e(tolerance).
sqrvif additionally adds the square roots
of the VIFs in e(sqrvif).
estadd pcorr [, semi ]
adds the partial correlations (see help ) and, optionally, the
semi-partial correlations between the dependent variable and the
individual regressors (see, e.g., the pcorr2 package from the SSC
Archive). In the case of multiple-equations models, the results are
computed for the first equation only. The partial correlations will
be returned in e(pcorr) and, if semi is specified, the semi-partial
correlations will be returned in e(spcorr).
estadd expb [, noconstant ]
adds in e(expb) the exponentiated coefficients (see the help
). noconstant excludes the constant from the added
estadd ebsd
adds in e(ebsd) the standardized factor change coefficients, i.e.
exp(b_jS_j), where b_j is the raw coefficient and S_j is the standard
deviation of regressor j, that are sometimes reported for logistic
regression (see Long 1997).
estadd mean
adds in e(mean) the means of the regressors.
estadd sd [, nobinary ]
adds in e(sd) the standard deviations of the regressors.
suppresses the computation of the standard deviation for 0/1
variables.
estadd summ [, stats ]
adds vectors of the regressors' descriptive statistics to the
estimates. The following stats are available:
description
-----------------------------------------------------------
range = max - min
standard deviation
coefficient of variation (sd/mean)
standard error of mean = sd/sqrt(n)
1st percentile
5th percentile
10th percentile
25th percentile
50th percentile
75th percentile
90th percentile
95th percentile
99th percentile
interquartile range = p75 - p25
all of the above
equivalent to specifying "p50"
equivalent to specifying "p25 p50 p75"
-----------------------------------------------------------
The default is mean sd min max. Alternatively, indicate the desired
statistics. For example, to add information on the regressors'
skewness and kurtosis, type
. estadd summ, skewness kurtosis
The statistics names are used as the names for the returned e()
matrices. For example, estadd summ, mean will store the means of the
regressors in e(mean).
+--------------------+
----+ Summary statistics +-----------------------------------------------
estadd coxsnell
adds in e(coxsnell) the Cox & Snell pseudo R-squared, which is
defined as
r2_coxsnell = 1 - ( L0 / L1 )^(2/N)
where L0 is the likelihood of the model without regressors, L1 the
likelihood of the full model, and N is the sample size.
estadd nagelkerke
adds in e(nagelkerke) the Nagelkerke pseudo R-squared (or Cragg &
Uhler pseudo R-squared), which is defined as
r2_nagelkerke = r2_coxsnell / (1 - L0^(2/N))
estadd lrtest model [, name(string)
adds the results from a likelihood-ratio test, where model is the
comparison model (see help ). Added are e(lrtest_chi2),
e(lrtest_df), and e(lrtest_p). The names may be modified using the
name() option. Specify name(myname) to add e(mynamechi2),
e(mynamedf), and e(mynamep). See help
for the lrtest_options.
estadd ysumm [, stats ]
adds descriptive statistics of the dependent variable. See the
subcommand above for a list of the available stats. The default is
mean sd min max. The default prefix for the names of the added
scalars is y (e.g. the mean of the dependent variable will be
returned in e(ymean)). Use estadd's prefix() option to change the
prefix. If a model has multiple dependent variables, results for the
first variable will be added.
----+ Other +------------------------------------------------------------
estadd margins [marginlist] [if] [in] [weight] [, options ]
adds results from the margins command, which was introduced in Stata
11. See help
for options. All results returned by margins
except e(V) are added using "margins_" as a default prefix. For
example, the margins are added in e(margins_b). The standard errors
are added in e(margins_se). Use the
option to change the
default prefix.
----+ SPost +------------------------------------------------------------
The following subcommands are wrappers for commands from Long and
package (see http://www.indiana.edu/~jslsoc/spost.htm).
. net from http://www.indiana.edu/~jslsoc/stata
to obtain the latest SPost version (spost9_ado). SPost for Stata 8
(spostado) is not supported.
For examples on using the subcommands see
http://repec.org/bocode/e/estout/spost.html.
estadd brant [,
from Long and Freese's
package and adds the
returned results to e(). You may specify brant_options as described
in help . The following results are added:
------------------------------------------------------------
brant_chi2
Chi-squared of overall Brant test
Degrees of freedom of overall Brant test
P-value of overall Brant test
Test results for individual regressors
(rows: chi2, p&chi2)
------------------------------------------------------------
To address the rows of e(brant) in 's cells() option type
brant[chi2] and brant[p&chi2].
estadd fitstat [,
from Long and Freese's
package and adds the
returned scalars to e(). You may specify fitstat_options as described
in help . Depending on model and options, a selection of the
following scalar statistics is added:
------------------------------------------------------------
Deviance (D)
Degrees of freedom of D
LR or Wald X2
Degrees of freedom of X2
Prob & LR or Wald X2
Adjusted R2
McFadden's R2
McFadden's Adj R2
ML (Cox-Snell) R2
Cragg-Uhler(Nagelkerke) R2
McKelvey & Zavoina's R2
Efron's R2
Variance of y*
Variance of error
Adj Count R2
BIC used by Stata
AIC used by Stata
Number of rhs variables
Number of parameters
------------------------------------------------------------
estadd listcoef [varlist] [, nosd
from Long and Freese's
package and adds the
returned results to e(). You may specify listcoef_options as
described in help . Furthermore, option nosd suppresses
adding the standard deviations of the variables in e(b_sdx).
Depending on the estimation command and options, several of the
following matrices are added:
------------------------------------------------------------
x-standardized coefficients
y-standardized coefficients
Fully standardized coefficients
Factor change coefficients
Standardized factor change coefficients
Percent change coefficients
Standardized percent change coefficients
Standard deviation of the Xs
------------------------------------------------------------
For nominal models (, ) the original parametrization of
e(b) may not match the contrasts computed by listcoef. To be able to
tabulate standardized coefficients along with the raw coefficients
for the requested contrasts, the following additional matrices are
added for these models:
------------------------------------------------------------
raw coefficients
standard errors of raw coefficients
z statistics
------------------------------------------------------------
estadd mlogtest [varlist] [,
from Long and Freese's
package and adds the
returned results to e(). You may specify mlogtest_options as
described in help .
Depending on the specified options, a selection of the following
returns are added:
------------------------------------------------------------
hausman_set#_chi2
Hausman IIA tests using
hausman_set#_df
hausman_set#_p
suest_set#_chi2
Hausman IIA tests using
suest_set#_df
suest_set#_p
smhsiao_set#_chi2
Small-Hsiao IIA tests
smhsiao_set#_df
smhsiao_set#_p
combine_#1_#2_chi2 Wald tests for combination of outcomes
combine_#1_#2_df
combine_#1_#2_p
lrcomb_#1_#2_chi2
LR tests for combination of outcomes
lrcomb_#1_#2_df
lrcomb_#1_#2_p
wald_set#_chi2
Wald tests for sets of independent
wald_set#_df
wald_set#_p
lrtest_set#_chi2
LR tests for sets of independent
lrtest_set#_df
lrtest_set#_p
Wald tests for individual variables
(rows: chi2, df, p)
LR tests for individual variables
(rows: chi2, df, p)
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To address the rows of e(wald) and e(lrtest) in 's cells() option
type the row names in brackets, for example, wald[p] or lrtest[chi2].
estadd prchange [varlist] [if exp] [in range] [, pattern(typepattern)
binary(type) continuous(type) [no]avg split[(prefix)]
from Long and Freese's
package and adds the
returned results to e(). You may specify prchange_options as
described in help . In particular, the outcome() option may
be used with models for count, ordered, or nominal outcomes to
request results for a specific outcome. Further options are:
pattern(typepattern), binary(type), and continuous(type) to determine
which types of discrete change effects are added as the main
results. The default is to add the 0 to 1 change effect for
binary variables and the standard deviation change effect for
continuous variables. Use binary(type) and continuous(type) to
change these defaults. Available types are:
Description
------------------------------------------------
minimum to maximum change effect
0 to 1 change effect
delta() change effect
standard deviation change effect
marginal effect (some models only)
------------------------------------------------
Use pattern(typepattern) if you want to determine the type of the
added effects individually for each regressor. For example,
pattern(minmax sd delta) would add minmax for the first
regressor, sd for the second, and delta for the third, and then
proceed using the defaults for the remaining variables.
avg to request that only the average results over all outcomes are
added if applied to ordered or nominal models (, ,
, , ). The default is to add the average
results as well as the individual results for the different
outcomes (unless 's outcome() option is specified, in
which case only results for the indicated outcome are added).
Furthermore, specify noavg to suppress the average results and
only add the outcome-specific results. avg cannot be combined
with split or outcome().
split[(prefix)] to save each outcome's results in a separate
estimation set if applied to ordered or nominal models (,
, , , ). The estimation sets are named
prefix#, where # is the value of the outcome at hand. If no
prefix is provided, the name of the estimation set followed by an
underscore is used as the prefix. If the estimation set has no
name (because it has not been stored yet) the name of the
estimation command followed by an underscore is used as the
prefix (e.g. ologit_). The estimation sets stored by the split
option are intended for tabulation only and should not be used
with other post-estimation commands.
Depending on model and options, several of the following matrices and
scalars are added:
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1 if effects are centered, 0 else
Value of delta()
predval[#]
Prediction(s) at the base values
Outcome value (outcome()/split only)
Discrete change effects (rows: main, minmax,
01, delta, sd [, margefct])
Types of effects in the main row of e(dc)
Base values and descriptive statistics
(rows: X, SD, Min, Max)
------------------------------------------------------------
The e(dc) and e(X) matrices have multiple rows. The e(dc) matrix
contains the main results as determined by pattern(), binary(), and
continuous() in the first row.
The second and following rows contain
the separate results for each type of effect using the labels
provided by prchange as row names. Type dc[#] or dc[rowname] to
address the rows in 's cells() option, where # is the row
number or rowname is the row name. For example, type dc[-+sd/2] to
address the centered standard deviation change effects. To tabulate
the main results (1st row), simply type dc. e(pattern) indicates the
types of effects contained in the main row of e(dc) using numeric
codes. The codes are 1 for the minimum to maximum change effect, 2
for the 0 to 1 change effect, 3 for the delta() change effect, 4 for
the standard deviation change effect, and 5 for the marginal effect.
e(X) has four rows containing the base values, standard deviations,
minimums, and maximums. If the fromto option is specified, two
additional matrices, e(dcfrom) and e(dcto) are added.
estadd prvalue [if exp] [in range] [, label(string)
estadd prvalue post [name] [, title(string) swap ]
from Long and Freese's
package and adds the
returned results to e(). The procedure is to first collect a series
of predictions by repeated calls to estadd prvalue and then apply
estadd prvalue post to prepare the results for tabulation as in the
following example:
. logit lfp k5 k618 age wc hc lwg inc
. estadd prvalue, x(inc 10) label(low inc)
. estadd prvalue, x(inc 20) label(med inc)
. estadd prvalue, x(inc 30) label(high inc)
. estadd prvalue post
You may specify prvalue_options with estadd prvalue as described in
help . For example, use x() and rest() to set the values of
the independent variables. Use label() to label the single calls.
"pred#" is used as label if label() is omitted, where # is the number
of the call. Labels may contain spaces but they will be trimmed to a
maximum length of 30 characters and some characters (:, ., ") will be
replaced by underscore. The results from the single calls are
collected in matrix e(_estadd_prvalue) (predictions) and matrix
e(_estadd_prvalue_x) (x-values). Specify replace to drop results from
previous calls.
estadd prvalue post posts the collected predictions in e(b) so that
they can be tabulated. The following results are saved:
------------------------------------------------------------
number of observations
name of dependent variable
estadd_prvalue
model estimation command
properties
predictions
standard errors
lower confidence interval bounds
upper confidence interval bounds
outcome values
conditional predictions (some models only)
values of predictors (for each prediction)
second equation predictors (some models only)
------------------------------------------------------------
estadd prvalue post replaces the current model unless name is
specified, in which case the results are stored under name and the
model remains active. However, if the model has a name (because it
has been stored), the name of the model is used as a prefix.
example, the model has been stored as model1, then estadd prvalue
post stores its results under model1name.
Use title() to specify a
title for the stored results.
The default for estadd prvalue post is to arrange e(b) in a way so
that predictions are grouped by outcome (i.e. outcome labels are used
as equations). Alternatively, specify swap to group predictions by
prvalue calls (i.e. to use the prediction labels as equations).
e(X) contains one row for each independent variable. To address the
rows in 's cells() option type X[varname], where varname is the
name of the variable of interest. e(X2), if provided, is analogous to
estadd asprvalue [, label(string)
estadd asprvalue post [name] [, title(string) swap ]
from Long and Freese's
package and adds the
returned results to e(). The procedure is to first collect a series
of predictions by repeated calls to estadd asprvalue and then apply
estadd asprvalue post to prepare the results for tabulation as in the
following example:
. clogit choice train bus time invc, group(id)
. estadd asprvalue, cat(train bus) label(at means)
. estadd asprvalue, cat(train bus) rest(asmean) label(at asmeans)
. estadd asprvalue post
You may specify asprvalue_options with estadd asprvalue as described
in help . For example, use x() and rest() to set the values
of the independent variables.
Use label() to label the single calls.
"pred#" is used as label if label() is omitted, where # is the number
of the call.
Labels may contain spaces but they will be trimmed to a
maximum length of 30 characters and some characters (:, ., ") will be
replaced by underscore. The results from the single calls are
collected in matrices e(_estadd_asprval) (predictions),
e(_estadd_asprval_asv) (values of alternative-specific variables),
and e(_estadd_asprval_csv) (values of case-specific variables).
Specify replace to drop results from previous calls.
estadd asprvalue post posts the collected predictions in e(b) so that
they can be tabulated. The following results are saved:
------------------------------------------------------------
number of observations
name of dependent variable
estadd_asprvalue
model estimation command
properties
predictions
alternative-specific variables (if available)
case-specific variables (if available)
------------------------------------------------------------
estadd asprvalue post replaces the current model unless name is
specified, in which case the results are stored under name and the
model remains active. However, if the model has a name (because it
has been stored), the name of the model is used as a prefix.
example, the model has been stored as model1, then estadd asprvalue
post stores its results under model1name.
Use title() to specify a
title for the stored results.
The default for estadd asprvalue post is to arrange e(b) in a way so
that predictions are grouped by outcome (i.e. outcome labels are used
as equations). Alternatively, specify swap to group predictions by
prvalue calls (i.e. to use the prediction labels as equations).
e(asv) and e(csv) contain one row for each variable.
To address the
rows in 's cells() option type asv[varname] or csv[varname],
where varname is the name of the variable of interest.
replace permits estadd to overwrite existing e() macros, scalars, or
prefix(string) denotes a prefix for the names of the added results. The
default prefix is an empty string. For example, if prefix(string) is
specified, the beta subcommand will return the matrix e(stringbeta).
quietly suppresses the output from the called subcommand and displays
only the list of added results. Note that many of estadd's
subcommands do not generate output, in which case quietly has no
subcmdopts are subcommand specific options. See the descriptions of the
subcommands above.
Example 1: Add r()-returns from other programs to the current estimates
. sysuse auto
(1978 Automobile Data)
. quietly regress price mpg weight
. test mpg=weight
mpg - weight = 0
Prob & F =
. estadd scalar p_diff = r(p)
added scalar:
e(p_diff) =
. estout, stats(p_diff)
-------------------------
-------------------------
-------------------------
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Example 2: Add means and standard deviations of the model's regressors to
the current estimates
. quietly logit foreign price mpg
. estadd summ, mean sd
added matrices:
. estout, cells("mean sd") drop(_cons)
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Example 3: Add standardized beta coefficients to stored estimates
. eststo: quietly regress price mpg
(est1 stored)
. eststo: quietly regress price mpg foreign
(est2 stored)
. estadd beta: *
. estout, cells(beta)
drop(_cons)
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See http://repec.org/bocode/e/estout for additional examples.
Writing one's own subcommands
A program providing a new estadd subcommand should be called
estadd_mysubcommand (see help
for advice on defining programs).
mysubcommand will be available to estadd as a new subcommand after the
program definition has been executed or saved to a file called
"estadd_mysubcommand.ado" in either the current directory or somewhere
else in the adopath (see help ).
Use the subcommands provided within "estadd.ado" as a starting point for
writing new subcommands. See
http://repec.org/bocode/e/estout/estadd.html#estadd007 for an example.
Ben Jann, Institute of Sociology, University of Bern, jann@soz.unibe.ch
[R] estimates
help for , , , , , ,}