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你的看xttobit里面的predict的选项
你对比一下tobit的选项
让你看手册和帮助，这些都是里面的例子啊
Title
[XT] xttobit postestimation -- Postestimation tools for xttobit
Description
The following postestimation commands are available after xttobit:
Command Description
--------------------------------------------------------------------------------------------------------------
contrast contrasts and ANOVA-style joint tests of estimates
estat ic Akaike's and Schwarz's Bayesian information criteria (AIC and BIC)
estat summarize summary statistics for the estimation sample
estat vce variance-covariance matrix of the estimators (VCE)
estimates cataloging estimation results
forecast dynamic forecasts and simulations
lincom point estimates, standard errors, testing, and inference for linear combinations of
coefficients
lrtest likelihood-ratio test
margins marginal means, predictive margins, marginal effects, and average marginal effects
marginsplot graph the results from margins (profile plots, interaction plots, etc.)
nlcom point estimates, standard errors, testing, and inference for nonlinear combinations of
coefficients
predict predictions, residuals, influence statistics, and other diagnostic measures
predictnl point estimates, standard errors, testing, and inference for generalized predictions
pwcompare pairwise comparisons of estimates
test Wald tests of simple and composite linear hypotheses
testnl Wald tests of nonlinear hypotheses
--------------------------------------------------------------------------------------------------------------
Syntax for predict
predict [type] newvar [if] [in] [, statistic nooffset]
statistic Description
--------------------------------------------------------------------------------------------------------------
Main
xb linear prediction assuming u_i=0, the default
stdp standard error of the linear prediction
stdf standard error of the linear forecast
pr0(a,b) Pr(a < y < b) assuming u_i is zero
e0(a,b) E(y | a < y < b) assuming u_i is zero
ystar0(a,b) E(y*), y*=max{a,min(y_j,b)} assuming u_i=0
--------------------------------------------------------------------------------------------------------------
These statistics are available both in and out of sample; type predict ... if e(sample) ... if wanted only for
the estimation sample.
where a and b may be numbers or variables; a missing (a > .) means minus infinity, and b missing (b > .) means
plus infinity; see missing.
Menu for predict
Statistics > Postestimation > Predictions, residuals, etc.
Options for predict
+------+
----+ Main +--------------------------------------------------------------------------------------------------
xb, the default, calculates the linear prediction.
stdp calculates the standard error of the linear prediction. It can be thought of as the standard error of
the predicted expected value or mean for the observation's covariate pattern. The standard error of the
prediction is also referred to as the standard error of the fitted value.
stdf calculates the standard error of the linear forecast. This is the standard error of the point prediction
for 1 observation. It is commonly referred to as the standard error of the future or forecast value. By
construction, the standard errors produced by stdf are always larger than those produced by stdp; see
Methods and formulas in [R] regress.
pr0(a,b) calculates estimates of Pr(a < y < b) assuming u_i is zero, which is the probability that y would be
observed in the interval (a,b), given the current values of the predictors, x_it, and given a zero random
effect. In the discussion that follows, these two conditions are implied.
a and b may be specified as numbers or variable names;
pr0(20,30) calculates Pr(20 < y < 30);
pr0(lb,ub) calculates Pr(lb < y < ub); and
pr0(20,ub) calculates Pr(20 < y < ub).
a missing (a > .) means minus infinity; pr0(.,30) calculates Pr(y < 30) and pr0(lb,30) calculates
Pr(y < 30) in observations for which lb > . (and calculates Pr(lb < y < 30) elsewhere).
b missing (b > .) means plus infinity; pr0(20,.) calculates Pr(y > 20) and pr0(20,ub) calculates
Pr(y > 20) in observations for which ub > . (and calculates Pr(20 < y < ub) elsewhere).
e0(a,b) calculates estimates of E(y | a < y < b) assuming u_i is zero, which is the expected value of y
conditional on y being in the interval (a,b), meaning that y is truncated. a and b are specified as they
are for pr0().
ystar0(a,b) calculates estimates of E(Y*) assuming u_i is zero, where Y* = a if y < a, Y* = b if y > b, and
Y* = y otherwise, meaning that Y* is the censored version of y. a and b are specified as they are for
pr0().
nooffset is relevant only if you specified offset(varname) for xttobit. It modifies the calculations made by
predict so that they ignore the offset variable; the linear prediction is treated as xb rather than xb +
offset.
Examples
Setup
. webuse nlswork3
. xtset idcode
. xttobit ln_wage union age grade not_smsa south##c.year, ul(1.9)
Average marginal effect of age on expected log wage, conditional on log wage being less than 1.9
. margins, predict(e0(., 1.9)) dydx(age)
Title
[R] tobit postestimation -- Postestimation tools for tobit
Description
The following postestimation commands are available after tobit:
Command Description
--------------------------------------------------------------------------------------------------------------
contrast contrasts and ANOVA-style joint tests of estimates
estat ic Akaike's and Schwarz's Bayesian information criteria (AIC and BIC)
estat summarize summary statistics for the estimation sample
estat vce variance-covariance matrix of the estimators (VCE)
estat (svy) postestimation statistics for survey data
estimates cataloging estimation results
(1) forecast dynamic forecasts and simulations
hausman Hausman's specification test
lincom point estimates, standard errors, testing, and inference for linear combinations of
coefficients
linktest link test for model specification
(2) lrtest likelihood-ratio test
margins marginal means, predictive margins, marginal effects, and average marginal effects
marginsplot graph the results from margins (profile plots, interaction plots, etc.)
nlcom point estimates, standard errors, testing, and inference for nonlinear combinations of
coefficients
predict predictions, residuals, influence statistics, and other diagnostic measures
predictnl point estimates, standard errors, testing, and inference for generalized predictions
pwcompare pairwise comparisons of estimates
suest seemingly unrelated estimation
test Wald tests of simple and composite linear hypotheses
testnl Wald tests of nonlinear hypotheses
--------------------------------------------------------------------------------------------------------------
(1) forecast is not appropriate with svy estimation results.
(2) lrtest is not appropriate with svy estimation results.
Syntax for predict
predict [type] newvar [if] [in] [, statistic nooffset]
predict [type] {stub*|newvar_reg newvar_sigma} [if] [in] , scores
statistic Description
--------------------------------------------------------------------------------------------------------------
Main
xb linear prediction; the default
stdp standard error of the linear prediction
stdf standard error of the forecast
pr(a,b) Pr(a < y < b)
e(a,b) E(y|a < y < b)
ystar(a,b) E(y*),y* = max{a, min(y,b)}
--------------------------------------------------------------------------------------------------------------
These statistics are available both in and out of sample; type predict ... if e(sample) ... if wanted only for
the estimation sample.
stdf is not allowed with svy estimation results.
where a and b may be numbers or variables; a missing (a > .) means minus infinity, and b missing (b > .) means
plus infinity; see missing.
Menu for predict
Statistics > Postestimation > Predictions, residuals, etc.
Options for predict
+------+
----+ Main +--------------------------------------------------------------------------------------------------
xb, the default, calculates the linear prediction.
stdp calculates the standard error of the prediction, which can be thought of as the standard error of the
predicted expected value or mean for the observation's covariate pattern. The standard error of the
prediction is also referred to as the standard error of the fitted value.
stdf calculates the standard error of the forecast, which is the standard error of the point prediction for 1
observation. It is commonly referred to as the standard error of the future or forecast value. By
construction, the standard errors produced by stdf are always larger than those produced by stdp; see
Methods and formulas in [R] regress.
pr(a,b) calculates Pr(a < xb + u < b), the probability that y|x would be observed in the interval (a,b).
a and b may be specified as numbers or variable names; lb and ub are variable names;
pr(20,30) calculates Pr(20 < xb + u < 30);
pr(lb,ub) calculates Pr(lb < xb + u < ub); and
pr(20,ub) calculates Pr(20 < xb + u < ub).
a missing (a > .) means minus infinity; pr(.,30) calculates Pr(-infinity < xb + u < 30);
pr(lb,30) calculates Pr(-infinity < xb + u < 30) in observations for which lb > .
and calculates Pr(lb < xb + u < 30) elsewhere.
b missing (b > .) means plus infinity; pr(20,.) calculates Pr(+infinity > xb + u > 20);
pr(20,ub) calculates Pr(+infinity > xb + u > 20) in observations for which ub > .
and calculates Pr(20 < xb + u < ub) elsewhere.
e(a,b) calculates E(xb + u | a < xb + u < b), the expected value of y|x conditional on y|x being in the
interval (a,b), meaning that y|x is truncated. a and b are specified as they are for pr().
ystar(a,b) calculates E(y*), where y* = a if xb + u < a, y* = b if xb + u > b, and y* = xb+u otherwise,
meaning that y* is censored. a and b are specified as they are for pr().
nooffset is relevant only if you specified offset(varname). It modifies the calculations made by predict so
that they ignore the offset variable; the linear prediction is treated as xb rather than xb + offset.
scores calculates equation-level score variables.
The first new variable will contain the derivative of the log likelihood with respect to the regression
equation.
The second new variable will contain the derivative of the log likelihood with respect to the scale
equation (sigma).
Examples
Setup
. sysuse auto
. generate wgt = weight/100
. tobit mpg wgt, ll(17) ul(24)
Average marginal effects for all covariates
. margins, dydx(*)
Marginal effect on the truncated expected value, conditional on weights of 2000 and 2500 pounds
. margins, dydx(wgt) predict(e(17,24)) at(wgt=(20 25))
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