Tobit回归之后怎样求回归残差值？谢谢！

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如题，我需要对估计模型进行tobit回归，因变量为>=0的数。
但是回归之后，我进行 predict var1, res 的时候提示出错 “option res not allowed”

请问这样的问题应该怎么处理？

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关键词：Tobit回归 Tobit bit allowed predict

Stata不提供tobit残差的命令（tobit的残差与一般回归的不同）
只提供了各种预测值的命令



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).

楼主，你好，请问一下你tobit回归后怎么得到残差的问题解决了吗？？我现在也是遇到这个问题了，想问一下你是怎么解决的，谢谢！

skxkay 发表于 2017-8-12 15:23
楼主，你好，请问一下你tobit回归后怎么得到残差的问题解决了吗？？我现在也是遇到这个问题了，想问一下你是 ...

没有，貌似tobit回归一般不求残值