07051029 发表于 2009-6-18 22:02 
请问高手 面板里面怎么做Probit估计呢？
xtprobit
help xtprobit dialog: xtprobit
also see: xtprobit postestimation
-------------------------------------------------------------------------------
Title
[XT] xtprobit -- Random-effects and population-averaged probit models
Syntax
Random-effects (RE) model
xtprobit depvar [indepvars] [if] [in] [weight] [, re RE_options]
Population-averaged (PA) model
xtprobit depvar [indepvars] [if] [in] [weight] , pa [PA_options]
RE_options description
-------------------------------------------------------------------------
Model
noconstant suppress constant term
re use random-effects estimator; the default
offset(varname) include varname in model with coefficient
constrained to 1
constraints(constraints) apply specified linear constraints
collinear keep collinear variables
SE
vce(vcetype) vcetype may be oim, bootstrap, or jackknife
Reporting
level(#) set confidence level; default is level(95)
noskip perform likelihood-ratio test
Int opts (RE)
intmethod(intmethod) integration method; intmethod may be
mvaghermite, aghermite, or ghermite;
default is intmethod(mvaghermite)
intpoints(#) use # quadrature points; default is
intpoints(12)
Max options
maximize_options control the maximization process; seldom
used
-------------------------------------------------------------------------
PA_options description
-------------------------------------------------------------------------
Model
noconstant suppress constant term
pa use population-averaged estimator
offset(varname) include varname in model with coefficient
constrained to 1
Correlation
corr(correlation) within-group correlation structure
force estimate even if observations unequally
spaced in time
SE/Robust
vce(vcetype) vcetype may be conventional, robust,
bootstrap, or jackknife
nmp use divisor N-P instead of the default N
scale(parm) override the default scale parameter; parm
may be x2, dev, phi, or #
Reporting
level(#) set confidence level; default is level(95)
Opt options
optimize_options control the optimization process; seldom
used
-------------------------------------------------------------------------
correlation description
-------------------------------------------------------------------------
exchangeable exchangeable
independent exchangeable
unstructured unstructured
fixed matname user-specified
ar # autoregressive of order #
stationary # stationary of order #
nonstationary # nonstationary of order #
-------------------------------------------------------------------------
A panel variable must be specified. For xtprobit, pa, correlation
structures other than exchangeable and independent require that a time
variable also be specified. Use xtset.
depvar and indepvars may contain time-series operators; see tsvarlist.
by, statsby, and xi are allowed; see prefix.
iweights, fweights, and pweights are allowed for the population-averaged
model, and iweights are allowed in the random-effects model; see
weight. Weights must be constant within panel.
See [XT] xtprobit postestimation for features available after estimation.
Description
xtprobit fits random-effects and population-averaged probit models.
There is no command for a conditional fixed-effects model, as there does
not exist a sufficient statistic allowing the fixed effects to be
conditioned out of the likelihood. Unconditional fixed-effects probit
models may be fitted with probit command with indicator variables for the
panels. The appropriate indicator variables can be generated using
tabulate or xi. However, unconditional fixed-effects estimates are
biased.
By default, the population-averaged model is an equal-correlation model;
xtprobit assumes corr(exchangeable). See [XT] xtgee for information on
how to fit other population-averaged models.
See logistic estimation commands for a list of related estimation
commands.
Options for RE model
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [XT] estimation options.
re requests the random-effects estimator. re is the default if neither
re not pa is specified.
offset(varname), constraints(constraints), collinear; see [XT] estimation
options.
+----+
----+ SE +---------------------------------------------------------------
vce(vcetype) specifies the type of standard error reported, which
includes types that are derived from asymptotic theory and that use
bootstrap or jackknife methods; see [XT] vce_options.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#), noskip; see [XT] estimation options.
+---------------+
----+ Int opts (RE) +----------------------------------------------------
intmethod(intmethod), intpoints(#); see [XT] estimation options.
+-------------+
----+ Max options +------------------------------------------------------
maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, shownrtolerance,
tolerance(#), ltolerance(#), gtolerance(#), nrtolerance(#),
nonrtolerance, from(init_specs); see [R] maximize. Some of these
options are not available if intmethod(ghermite) is specified. These
options are seldom used.
Options for PA model
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [XT] estimation options.
pa requests the population-averaged estimator.
offset(varname); see [XT] estimation options.
+-------------+
----+ Correlation +------------------------------------------------------
corr(correlation), force; see [XT] estimation options.
+-----------+
----+ SE/Robust +--------------------------------------------------------
vce(vcetype) specifies the type of standard error reported, which
includes types that are derived from asymptotic theory, that are
robust to some kinds of misspecification, and that use bootstrap or
jackknife methods; see [XT] vce_options.
vce(conventional), the default, uses the conventionally derived
variance estimator for generalized least-squares regression.
nmp, scale(x2|dev|phi|#); see [XT] vce_options.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [XT] estimation options.
+-------------+
----+ Opt options +------------------------------------------------------
optimize_options control the iterative optimization process. These
options are seldom used.
iterate(#) specifies the maximum number of iterations. When the
number of iterations equals #, the optimization stops and presents
the current results, even if the convergence tolerance has not been
reached. The default value of iterate() is 100.
tolerance(#) specifies the tolerance for the coefficient vector.
When the relative change in the coefficient vector from one iteration
to the next is less than or equal to #, the optimization process is
stopped. tolerance(1e-6) is the default.
nolog suppress the display of the iteration log.
trace specifies that the current estimates should be printed at each
iteration.
Technical note
The random-effects model is calculated using quadrature, which is an
approximation whose accuracy depends partially on the number of
integration points used. We can use the quadchk command to see if
changing the number of integration points affects the results. If the
results change, the quadrature approximation is not accurate given the
number of integration points. Try increasing the number of integration
points using the intpoints() option and again run quadchk. Do not
attempt to interpret the results of estimates when the coefficients
reported by quadchk differ substantially. See [XT] quadchk for details
and [XT] xtprobit for an example.
Because the xtprobit, re likelihood function is calculated by Gauss
Hermite quadrature, on large problems, the computations can be slow.
Computation time is roughly proportional to the number of points used for
the quadrature.
Examples
Setup
. webuse union
Random-effects model
. xtprobit union age grade not_smsa south southXt
Equal-correlation population-averaged model
. xtprobit union age grade not_smsa south southXt, pa
Equal-correlation population-averaged model with robust variance
. xtprobit union age grade not_smsa south southXt, pa vce(robust)
Saved results
xtprobit, re saves the following in e():
Scalars
e(N) number of observations
e(N_g) number of groups
e(N_cd) number of completely determined obs.
e(df_m) model degrees of freedom
e(ll) log likelihood
e(ll_0) log likelihood, constant-only model
e(ll_0) log likelihood, comparison model
e(g_max) largest group size
e(g_min) smallest group size
e(g_avg) average group size
e(chi2) chi-squared
e(chi2_c) chi-squared for comparison test
e(rho) rho
e(sigma_u) panel-level standard deviation
e(n_quad) number of quadrature points
e(k) number of parameters
e(k_eq) number of equations
e(k_eq_model) number of equations in model Wald test
e(k_dv) number of dependent variables
e(p) significance
e(rank) rank of e(V)
e(rank0) rank of e(V) for constant-only model
e(ic) number of iterations
e(rc) return code
e(converged) 1 if converged, 0 otherwise
Macros
e(cmd) xtprobit
e(cmdline) command as typed
e(depvar) name of dependent variable
e(ivar) variable denoting groups
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(offset1) offset
e(chi2type) Wald or LR; type of model chi-squared test
e(chi2_ct) Wald or LR; type of model chi-squared test corresponding
to e(chi2_c)
e(intmethod) integration method
e(distrib) Gaussian; the distribution of the random effect
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(opt) type of optimization
e(ml_method) type of ml method
e(user) name of likelihood-evaluator program
e(technique) maximization technique
e(crittype) optimization criterion
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(ilog) iteration log
e(gradient) gradient vector
Functions
e(sample) marks estimation sample
xtprobit, pa saves the following in e():
Scalars
e(N) number of observations
e(N_g) number of groups
e(df_m) model degrees of freedom
e(df_pear) degrees of freedom from Pearson chi-squared
e(g_max) largest group size
e(g_min) smallest group size
e(g_avg) average group size
e(chi2) chi-squared
e(chi2_dev) chi-squared test of deviance
e(chi2_dis) chi-squared test of deviance dispersion
e(deviance) deviance
e(dispers) deviance dispersion
e(tol) target tolerance
e(dif) achieved tolerance
e(phi) scale parameter
e(rc) return code
Macros
e(cmd) xtgee
e(cmd2) xtprobit
e(cmdline) command as typed
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(depvar) name of dependent variable
e(family) binomial
e(link) probit; link function
e(corr) correlation structure
e(crittype) optimization criterion
e(scale) x2, dev, phi, or #; scale parameter
e(ivar) variable denoting groups
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(chi2type) Wald; type of model chi-squared test
e(offset) offset
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(R) estimated working correlation matrix
Functions
e(sample) marks estimation sample
Also see
Manual: [XT] xtprobit
Online: [XT] xtprobit postestimation;
[XT] quadchk, [XT] xtcloglog, [XT] xtgee, [XT] xtlogit, [R]
constraint, [R] probit
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