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# Multilevel tobit models

## Highlights

• Left-censoring, right-censoring, or both
• Censoring that varies by observation
• Random effects
• Random intercepts
• Random coefficients (slopes)
• Multilevel: two, three, or more levels
• Make inferences about either the uncensored or the censored outcome
• Support for complex survey data
• Graph marginal means and marginal effects
• Intraclass correlation
• Support for Bayesian estimation

The new metobit command fits multilevel and panel-data models for which the outcome is censored. Censored means that rather than the outcome $$y$$ being observed precisely in all observations, it is known only that $$y \leq y_l$$ (left-censoring) or $$y \geq y_u$$ (right-censoring) in some of the observations. For instance, the amount of a pollutant may be left-censored because the measurement instrument has a lower limit of detection. The number of attendees at an event may be right-censored because the stadium has a limited number of seats.

Multilevel means that the fitted model accounts for lack of independence within groups of observations, such as people who live near each other or students who attend the same school or students who are tested repeatedly. metobit can also fit models with multiple levels of nesting. You can fit models with data on students within school districts within cities and even have random effects for each level!

## Let's see it work

Tobit models, whether multilevel or one-level, can be used for two types of inference—for the entire population as if it were not censored and for the censored population.

We have been hired to analyze data on attendance at 500 soccer stadiums. The data are censored when the stadium is sold out. In such cases, it is likely that attendance would have been greater had there been more seats.

Clients who run stadiums, who could increase the number of seats at a cost, would be interested in analysis of the uncensored population. Clients who rent and cannot increase the number of seats are interested in analysis of the censored population.

We can use metobit to answer questions for both types of clients. In fact, we will fit the model once and use different predictions to answer different questions.

The data we have include attend, stadium attendance in thousands for each game played during the season. We will model attendance as a function of

• winp, the winning percentage of the local team;
• inter, the probability that the local team makes it to an international postseason;
• cost, the cost of a ticket for the game being played; and
• weather, whether a storm—rain or snow—was forecast on game day.

We could fit this model with a linear multilevel estimator but for the fact that each stadium has a seating limit. That limit is recorded in the variable max.

Using metobit, we will type

. metobit attend winp inter cost i.weather || stadium: winp, ul(max)


Option ul(max) specifies the upper-censoring point.

Right of the || is the level-2 ID variable, stadium. We are specifying that we want random intercepts for each stadium and random coefficients for winning percentage.

We fit our desired model:

. metobit attend winp inter cost i.weather || stadium: winp, ul(max)

Mixed-effects tobit regression                  Number of obs     =      8,643
Uncensored     =      6,735
Limits: lower = -inf                               Left-censored  =          0
upper = max                                Right-censored =      1,908

Group variable:         stadium                 Number of groups  =        500
Obs per group:
min =         12
avg =       17.3
max =         20

Integration method: mvaghermite                 Integration pts.  =          7

Wald chi2(4)      =   29846.04
Log likelihood = -21447.366                     Prob > chi2       =     0.0000

attend        Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

winp     .7125553   .0657861    10.83   0.000      .583617    .8414936
inter     .7572882   .0043945   172.33   0.000     .7486752    .7659013
cost    -.0075386   .0004743   -15.89   0.000    -.0084683    -.006609
1.weather     .1346941   .2741321     0.49   0.623     -.402595    .6719832
_cons     7.608423   .2590445    29.37   0.000     7.100705    8.116141

var(winp)    1.379988   .1280754                      1.150472    1.655291
var(_cons)    14.77107   1.306585                       12.4199    17.56732

var(e.attend)    22.31481   .4019761                       21.5407    23.11674

LR test vs. tobit model: chi2(2) = 5461.67                Prob >chi2 = 0.0000

From the model, we can obtain estimates of average attendance. There are many ways to calculate average attendance. What would be the uncensored average attendance if max had not been in effect? What is the predicted average attendance given max? What would be average attendance if seating was increased by 1,000 in all stadiums having more than 90% average attendance? 2,000? 3,000?

In a world where max was not relevant, average attendance would have been about 23,510:

. margins

Predictive margins                              Number of obs     =      8,131
Model VCE    : OIM

Expression   : Marginal linear prediction, predict()

Delta-method
Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]

_cons    23.50981   .3189898    73.70   0.000      22.8846    24.13502



In the real world where the current value of max is binding, it would be about 18,712:

. margins, predict(ystar(.,max))

Predictive margins                              Number of obs     =      8,131
Model VCE    : OIM

Expression   : E(attend*|attend<max), predict(ystar(.,max))

Delta-method
Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]

_cons     18.7123   .1790272   104.52   0.000     18.36141    19.06319



We could also use margins to answer what attendance would be if max was increased by 1,000 in stadiums with over 90% attendance.

. generate new_max = max + 1
. margins if attend_rate>.90, predict(ystar(.,new_max))

Predictive margins                              Number of obs     =      5,284
Model VCE    : OIM

Expression   : E(attend*|attend<new_max), predict(ystar(.,new_max))

Delta-method
Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]

_cons    26.81172   .3243089    82.67   0.000     26.17608    27.44735



Average attendance would be 26,812 in stadiums with attendance rate greater than 90%. This seems like a large number, but the stadiums in our sample with more than 90% attendance are the larger stadiums with teams with the highest winning percentage.

## Tell me more

You can also fit Bayesian multilevel tobit models using the bayes prefix.

Read more about multilevel tobit models in the Stata Multilevel Mixed-Effects Reference Manual; see [ME] metobit.