Stata 15 help for ivtobit

[R] ivtobit -- Tobit model with continuous endogenous covariates

Syntax

Maximum likelihood estimator

ivtobit depvar [varlist1] (varlist2 = varlist_iv) [if] [in] [weight] , ll[(#)] ul[(#)] [mle_options]

Two-step estimator

ivtobit depvar [varlist1] (varlist2 = varlist_iv) [if] [in] [weight], twostep ll[(#)] ul[(#)] [tse_options]

varlist1 is the list of exogenous variables.

varlist2 is the list of endogenous variables.

varlist_iv is the list of exogenous variables used with varlist1 as instruments for varlist2.

mle_options Description ------------------------------------------------------------------------- Model * ll[(#)] left-censoring limit * ul[(#)] right-censoring limit mle use conditional maximum-likelihood estimator; the default constraints(constraints) apply specified linear constraints

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, opg, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) first report first-stage regression nocnsreport do not display constraints display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Maximization maximize_options control the maximization process

coeflegend display legend instead of statistics ------------------------------------------------------------------------- * You must specify at least one of ll[(#)] and ul[(#)].

tse_options Description ------------------------------------------------------------------------- Model * twostep use Newey's two-step estimator; the default is mle * ll[(#)] left-censoring limit * ul[(#)] right-censoring limit

SE/Robust vce(vcetype) vcetype may be twostep, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) first report first-stage regression display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

coeflegend display legend instead of statistics ------------------------------------------------------------------------- * twostep is required. You must specify at least one of ll[(#)] and ul[(#)].

varlist1 and varlist_iv may contain factor variables; see fvvarlist. depvar, varlist1, varlist2, and varlist_iv may contain time-series operators; see tsvarlist. bootstrap, by, jackknife, rolling, statsby, and svy are allowed; see prefix. fp is allowed with the maximum likelihood estimator. Weights are not allowed with the bootstrap prefix. vce(), first, twostep, and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed with the maximum likelihood estimator. fweights are allowed with Newey's two-step estimator. See weight. coeflegend does not appear in the dialog box. See [R] ivtobit postestimation for features available after estimation.

Menu

Statistics > Endogenous covariates > Tobit model with endogenous covariates

Description

ivtobit fits tobit models where one or more of the covariates are endogenously determined. By default, ivtobit uses maximum likelihood estimation, but Newey's (1987) minimum chi-squared (two-step) estimator can be requested. Both estimators assume that the endogenous covariates are continuous and so are not appropriate for use with discrete endogenous covariates.

Options for ML estimator

+-------+ ----+ Model +------------------------------------------------------------

ll[(#)] and ul[(#)] indicate the lower and upper limits for censoring, respectively. You may specify one or both. Observations with depvar < ll() are left-censored; observations with depvar > ul() are right-censored; and remaining observations are not censored. You do not have to specify the censoring values at all. It is enough to type ll, ul, or both. When you do not specify a censoring value, ivtobit assumes that the lower limit is the minimum observed in the data (if ll is specified) and that the upper limit is the maximum (if ul is specified).

mle requests that the conditional maximum-likelihood estimator be used. This is the default.

constraints(constraints); see [R] estimation options.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim, opg), that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

first requests that the parameters for the reduced-form equations showing the relationships between the endogenous variables and instruments be displayed. For the two-step estimator, first shows the first-stage regressions. For the maximum likelihood estimator, these parameters are estimated jointly with the parameters of the tobit equation. The default is not to show these parameter estimates.

nocnsreport; see [R] estimation options.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and from(init_specs); see [R] maximize.

Setting the optimization type to technique(bhhh) resets the default vcetype to vce(opg).

The following option is available with ivtobit but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Options for two-step estimator

+-------+ ----+ Model +------------------------------------------------------------

twostep is required and requests that Newey's (1987) efficient two-step estimator be used to obtain the coefficient estimates.

ll[(#)] and ul[(#)] indicate the lower and upper limits for censoring, respectively. You may specify one or both. Observations with depvar < ll() are left-censored; observations with depvar > ul() are right-censored; and remaining observations are not censored. You do not have to specify the censoring values at all. It is enough to type ll, ul, or both. When you do not specify a censoring value, ivtobit assumes that the lower limit is the minimum observed in the data (if ll is specified) and that the upper limit is the maximum (if ul is specified).

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (twostep) and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

first requests that the parameters for the reduced-form equations showing the relationships between the endogenous variables and instruments be displayed. For the two-step estimator, first shows the first-stage regressions. For the maximum likelihood estimator, these parameters are estimated jointly with the parameters of the tobit equation. The default is to not show these parameter estimates.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

The following option is available with ivtobit but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

Setup . webuse laborsup

Obtain full ML estimates . ivtobit fem_inc fem_educ kids (other_inc = male_educ), ll . ivtobit fem_inc fem_educ kids (other_inc = male_educ), ll(12)

Obtain two-step estimates . ivtobit fem_inc fem_educ kids (other_inc = male_educ), ll twostep . ivtobit fem_inc fem_educ kids (other_inc = male_educ), ll(12) twostep

Stored results

ivtobit, mle stores the following in e():

Scalars e(N) number of observations e(N_unc) number of uncensored observations e(N_lc) number of left-censored observations e(N_rc) number of right-censored observations e(llopt) minimum of depvar or contents of ll() e(ulopt) maximum of depvar or contents of ul() e(k) number of parameters e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(k_aux) number of auxiliary parameters e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(ll) log likelihood e(N_clust) number of clusters e(endog_ct) number of endogenous covariates e(p) model Wald p-value e(p_exog) exogeneity test Wald p-value e(chi2) model Wald chi-squared e(chi2_exog) Wald chi-squared test of exogeneity e(rank) rank of e(V) e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) ivtobit e(cmdline) command as typed e(depvar) name of dependent variable e(instd) instrumented variables e(insts) instruments e(wtype) weight type e(wexp) weight expression e(title) title in estimation output 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(method) ml e(opt) type of optimization e(which) max or min; whether optimizer is to perform maximization or minimization e(ml_method) type of ml method e(user) name of likelihood-evaluator program e(technique) maximization technique e(properties) b V e(estat_cmd) program used to implement estat e(predict) program used to implement predict e(footnote) program used to implement the footnote display e(marginsok) predictions allowed by margins e(marginsprop) signals to the margins command e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(Sigma) Sigma hat e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample

ivtobit, twostep stores the following in e():

Scalars e(N) number of observations e(N_unc) number of uncensored observations e(N_lc) number of left-censored observations e(N_rc) number of right-censored observations e(llopt) contents of ll() e(ulopt) contents of ul() e(df_m) model degrees of freedom e(df_exog) degrees of freedom for chi-squared test of exogeneity e(p) model Wald p-value e(p_exog) exogeneity test Wald p-value e(chi2) model Wald chi-squared e(chi2_exog) Wald chi-squared test of exogeneity e(rank) rank of e(V)

Macros e(cmd) ivtobit e(cmdline) command as typed e(depvar) name of dependent variable e(instd) instrumented variables e(insts) instruments e(wtype) weight type e(wexp) weight expression e(chi2type) Wald; type of model chi-squared test e(vce) vcetype specified in vce() e(method) twostep e(properties) b V e(estat_cmd) program used to implement estat e(predict) program used to implement predict e(footnote) program used to implement the footnote display e(marginsok) predictions allowed by margins e(marginsprop) signals to the margins command 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

Reference

Newey, W. K. 1987. Efficient estimation of limited dependent variable models with endogenous explanatory variables. Journal of Econometrics 36: 231-250.


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