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  • tobit now accepts censoring limits and constraints
  • Some people think of tobit as being censored at zero. Stata's tobit estimation command allows you to specify the lower value of the censoring point and specify an upper censoring point. All that is unchanged. You can now specify censoring points—upper, lower, or both—that vary observation by observation. The censoring points can be stored in variables.

    tobit now allows constraints.

    tobit now has the other standard features that it always should have had, but this is just for completeness. You can, for instance, specify initial values.

  • tpoisson, ul()
  • The existing estimation command tpoisson fits truncated Poisson models. It previously fit only left-truncated models. It now fits left-, right-, and both-truncated models. New option ul() specifies the upper truncation limit.

  • One- and two-sample mean tests with clustered data
  • Existing command ztest has option cluster() and other options to account for clustering.

  • One- and two-sample proportion tests with clustered data
  • Existing command prtest has option cluster() and other options to account for clustering.

  • gsem fits truncated Poisson models
  • gsem, whether used to fit LCA models or the existing generalized SEM models, fits truncated Poisson models if you specify option family(poisson, ltruncated(...)).

  • Standard deviations and correlations instead of variances and covariances for multilevel models and generalized SEM
  • For multilevel models, estat sd displays random effects and within-group error parameter estimates as standard deviations and correlations instead of the variances and covariances reported in the estimation output.

    Similarly after gsem, estat sd reports the estimated variance components as standard deviations and correlations.

  • bayesmh has options for displaying results
    • bayesmh allows the eform and eform(string) options for reporting exponentiated coefficients such as odds ratios, incidence-rate ratios, and the like.
    • bayesmh allows option show(paramlist) to specify which model parameters should be presented in the output. Option show() joins existing option noshow(). Specify one, the other, or neither.
    • bayesmh allows option showreffects to specify that all random-effects estimates be presented in the output. They are not displayed by default.

  • Postestimation supports bayes: prefix command
  • If you use the bayes: prefix command with multilevel models such as mixed or meglm, then bayesgraph, bayesstats ess, and bayesstats summary have additional options.

    Option showreffects displays the results for all random-effects parameters.

    Option showreffects() displays specified random-effects parameters.

    By default, results are displayed for all model parameters except the random-effects parameters.

  • These estimation commands may be used with the svy: prefix:
  • CommandPurpose
    svy: asmixlogitAlternative-specific mixed logit regression
    svy: heckpoissonPoisson regression with sample selection
    svy: hetregressHeteroskedastic linear regression
    svy: stintregParametric interval-censored survival regression
    svy: zioprobitZero-inflated ordered probit
    svy: metobitMultilevel tobit regression
    svy: meintregMultilevel interval regression
    svy: eregressExtended linear regression
    svy: eintregExtended interval regression
    svy: eprobitExtended probit regression
    svy: eoprobitExtended ordered probit regression
    svy: gsemFor latent class analysis

  • The following existing estimation commands support combined use of svy: and fmm: to fit survey-adjusted finite mixture models:
  • CommandPurpose
    svy: fmm: regressLinear regression
    svy: fmm: tobitTobit regression
    svy: fmm: intregInterval regression
    svy: fmm: truncregTruncated regression
    svy: fmm: ivregressInstrumental-variable regression
    svy: fmm: logitLogistic regression
    svy: fmm: probitProbit regression
    svy: fmm: cloglogConditional log-log regression
    svy: fmm: ologitOrdered logistic regression
    svy: fmm: oprobitOrdered probit regression
    svy: fmm: mlogitMultinomial logistic regression
    svy: fmm: poissonPoisson regression
    svy: fmm: nbregNegative binomial regression
    svy: fmm: tpoissonTruncated Poisson regression
    svy: fmm: betaregBeta regression
    svy: fmm: glmGeneralized linear model
    svy: fmm: stregParametric survival regression

  • Cauchy distribution
  • A family of Cauchy distribution functions—cauchyden(), cauchy(), cauchytail(), invcauchy(), invcauchytail(), and lncauchyden()—compute the density, cumulative distribution, reverse cumulative distribution, inverse cumulative distribution, and natural logarithm of the density.

    rcauchy is a Cauchy random-number generator.

  • Laplace distribution
  • A family of Laplace distribution functions—laplaceden(), laplace(), laplacetail(), invlaplace(), invlaplacetail(), and lnlaplaceden()—compute the density, cumulative distribution, reverse cumulative distribution, inverse cumulative distribution, inverse reverse cumulative distribution, and natural logarithm of the density.

    rlaplace() is a Laplace random number generator.

  • Multivariate normal distribution
  • Mata functions are available for calculating values and derivatives of the multivariate normal distribution.





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