Stata 15 help for bayes_tobit

[BAYES] bayes: tobit -- Bayesian tobit regression


bayes [, bayesopts] : tobit depvar [indepvars] [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term ll[(varname|#)] left-censoring limit ul[(varname|#)] right-censoring limit offset(varname) include varname in model with coefficient constrained to 1 collinear keep collinear variables

Reporting display_options control spacing, line width, and base and empty cells

level(#) set credible level; default is level(95) ------------------------------------------------------------------------- indepvars may contain factor variables; see fvvarlist. depvar and indepvars may contain time-series operators; see tsvarlist. fweights are allowed; see weight. bayes: tobit, level() is equivalent to bayes, clevel(): tobit. For a detailed description of options, see Options in [R] tobit.

bayesopts Description ------------------------------------------------------------------------- Priors * normalprior(#) specify standard deviation of default normal priors for regression coefficients; default is normalprior(100) * igammaprior(# #) specify shape and scale of default inverse-gamma prior for variance; default is igammaprior(0.01 0.01) prior(priorspec) prior for model parameters; this option may be repeated dryrun show model summary without estimation

Simulation mcmcsize(#) MCMC sample size; default is mcmcsize(10000) burnin(#) burn-in period; default is burnin(2500) thinning(#) thinning interval; default is thinning(1) rseed(#) random-number seed exclude(paramref) specify model parameters to be excluded from the simulation results

Blocking * blocksize(#) maximum block size; default is blocksize(50) block(paramref[, blockopts]) specify a block of model parameters; this option may be repeated blocksummary display block summary * noblocking do not block parameters by default

Initialization initial(initspec) initial values for model parameters nomleinitial suppress the use of maximum likelihood estimates as starting values initrandom specify random initial values initsummary display initial values used for simulation * noisily display output from the estimation command during initialization

Adaptation adaptation(adaptopts) control the adaptive MCMC procedure scale(#) initial multiplier for scale factor; default is scale(2.38) covariance(cov) initial proposal covariance; default is the identity matrix

Reporting clevel(#) set credible interval level; default is clevel(95) hpd display HPD credible intervals instead of the default equal-tailed credible intervals eform[(string)] report exponentiated coefficients and, optionally, label as string batch(#) specify length of block for batch-means calculations; default is batch(0) saving(filename[, replace]) save simulation results to filename.dta nomodelsummary suppress model summary [no]dots suppress dots or display dots every 100 iterations and iteration numbers every 1,000 iterations; default is nodots dots(#[, every(#)]) display dots as simulation is performed [no]show(paramref) specify model parameters to be excluded from or included in the output notable suppress estimation table noheader suppress output header title(string) display string as title above the table of parameter estimates display_options control spacing, line width, and base and empty cells

Advanced search(search_options) control the search for feasible initial values corrlag(#) specify maximum autocorrelation lag; default varies corrtol(#) specify autocorrelation tolerance; default is corrtol(0.01) ------------------------------------------------------------------------- * Starred options are specific to the bayes prefix; other options are common between bayes and bayesmh. Options prior() and block() can be repeated. priorspec and paramref are defined in [BAYES] bayesmh. paramref may contain factor variables; see fvvarlist. See [BAYES] bayesian postestimation for features available after estimation. Model parameters are regression coefficients {depvar:indepvars} and variance {sigma2}. Use the dryrun option to see the definitions of model parameters prior to estimation. For a detailed description of bayesopts, see Options in [BAYES] bayes.


Statistics > Linear models and related > Bayesian regression > Tobit regression


bayes: tobit fits a Bayesian tobit regression to a censored continuous outcome; see [BAYES] bayes and [R] tobit for details.


Setup . sysuse auto . generate wgt = weight/1000

Fit Bayesian tobit regression, specifying a right-censoring limit of 24 . bayes: tobit mpg wgt, ul(24)

Increase the burn-in period to 5,000 from the default of 2,500 and the MCMC sample size to 15,000 from the default 10,000 . bayes, burnin(5000) mcmcsize(15000): tobit mpg wgt, ul(24)

Use standard deviation of 10 instead of 100 of the default normal prior for regression coefficients . bayes, normalprior(10): tobit mpg wgt, ul(24)

Same as above, but also specify random-number seed for reproducibility . bayes, normalprior(10) rseed(12345): tobit mpg wgt, ul(24)

Fit Bayesian tobit regression using uniform priors for all regression coefficients . bayes, prior({mpg:wgt _cons}, uniform(-100,100)): tobit mpg wgt, ul(24)

Same as above, but use a shortcut notation to refer to all regression coefficients . bayes, prior({mpg:}, uniform(-100,100)): tobit mpg wgt, ul(24)

Use different uniform priors for the intercept and the slope . bayes, prior({mpg:wgt}, uniform(-10,10)) prior({mpg:_cons}, uniform(-100,100)): tobit mpg wgt, ul(24)

Save MCMC results on replay . bayes, saving(mymcmc)

Stored results

See Stored results in [BAYES] bayesmh.

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