Tobit model

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The Tobit model is a statistical model proposed by James Tobin (1958) to describe the relationship between a non-negative dependent variable y_i and an independent variable (or vector) x_i.

The model supposes that there is a latent (i.e. unobservable) variable y_i^*. This variable linearly depends on x_i via a parameter (vector) \beta which determines the relationship between the independent variable (or vector) x_i and the latent variable y_i^* (just as in a linear model). In addition, there is a normally distributed error term u_i to capture random influences on this relationship. The observable variable y_i is defined to be equal to the latent variable whenever the latent variable is above zero and zero otherwise.

y_i = \begin{cases} y_i^* & \textrm{if} \; y_i^* >0 \\ 0 & \textrm{if} \; y_i^* \leq 0 \end{cases}

where y_i^* is a latent variable:

y_i^* = \beta x_i %2B u_i, u_i \sim N(0,\sigma^2) \,

Contents

Consistency

If the relationship parameter \beta is estimated by regressing the observed y_i on x_i , the resulting ordinary least squares regression estimator is inconsistent. It will yield a downwards-biased estimate of the slope coefficient and an upwards-biased estimate of the intercept. Takeshi Amemiya (1973) has proven that the maximum likelihood estimator suggested by Tobin for this model is consistent.

Interpretation

The \beta coefficient should not be interpreted as the effect of x_i on y_i, as one would with a linear regression model; this is a common error. Instead, it should be interpreted as the combination of (1) the change in y_i of those above the limit, weighted by the probability of being above the limit; and (2) the change in the probability of being above the limit, weighted by the expected value of y_i if above.[1]

Variations of the Tobit model

Variations of the Tobit model can be produced by changing where and when censoring occurs. Amemiya (1985) classifies these variations into five categories (Tobit type I - Tobit type V), where Tobit type I stands for the first model described above. Schnedler (2005) provides a general formula to obtain consistent likelihood estimators for these and other variations of the Tobit model.

Type I

The Tobit model is a special case of a censored regression model, because the latent variable y_i^* cannot always be observed while the independent variable x_i is observable. A common variation of the Tobit model is censoring at a value y_L different from zero:

y_i = \begin{cases} y_i^* & \textrm{if} \; y_i^* >y_L \\ y_L & \textrm{if} \; y_i^* \leq y_L. \end{cases}

Another example is censoring of values above y_U.

y_i = \begin{cases} y_i^* & \textrm{if} \; y_i^* <y_U \\ y_U & \textrm{if} \; y_i^* \geq y_U. \end{cases}

Yet another model results when y_i is censored from above and below at the same time.

y_i = \begin{cases} y_i^* & \textrm{if} \; y_L<y_i^* <y_U \\ y_L & \textrm{if} \; y_i^* \leq y_L \\ y_U & \textrm{if} \; y_i^* \geq y_U. \end{cases}

The rest of the models will be presented as being bounded from below at 0, though this can be generalized as we have done for Type I.

Type II

Type II Tobit models introduce a second latent variable.

y_{2i} = \begin{cases} y_{2i}^* & \textrm{if} \; y_{1i}^* >0 \\ 0 & \textrm{if} \; y_{1i}^* \leq 0. \end{cases}

Heckman (1987) falls into the Type II Tobit. In Type I Tobit, the latent variable absorb both the process of participation and 'outcome' of interest. Type II Tobit allows the process of participation/selection and the process of 'outcome' to be independent, conditional on x.

Type III

Type III introduces a second observed dependent variable.

y_{1i} = \begin{cases} y_{1i}^* & \textrm{if} \; y_{1i}^* >0 \\ 0 & \textrm{if} \; y_{1i}^* \leq 0. \end{cases}
y_{2i} = \begin{cases} y_{2i}^* & \textrm{if} \; y_{1i}^* >0 \\ 0 & \textrm{if} \; y_{1i}^* \leq 0. \end{cases}

The Heckman model falls into this type.

Type IV

Type IV introduces a third observed dependent variable and a third latent variable.

y_{1i} = \begin{cases} y_{1i}^* & \textrm{if} \; y_{1i}^* >0 \\ 0 & \textrm{if} \; y_{1i}^* \leq 0. \end{cases}
y_{2i} = \begin{cases} y_{2i}^* & \textrm{if} \; y_{1i}^* >0 \\ 0 & \textrm{if} \; y_{1i}^* \leq 0. \end{cases}
y_{3i} = \begin{cases} y_{3i}^* & \textrm{if} \; y_{1i}^* >0 \\ 0 & \textrm{if} \; y_{1i}^* \leq 0. \end{cases}

Type V

Similar to Type II, in Type V we only observe the sign of y_{1i}^*.

y_{2i} = \begin{cases} y_{2i}^* & \textrm{if} \; y_{1i}^* >0 \\ 0 & \textrm{if} \; y_{1i}^* \leq 0. \end{cases}
y_{3i} = \begin{cases} y_{3i}^* & \textrm{if} \; y_{1i}^* >0 \\ 0 & \textrm{if} \; y_{1i}^* \leq 0. \end{cases}

The likelihood function

Below are the likelihood and log likelihood functions for a type I Tobit. This is a Tobit that is censored from below at y_L when the latent variable y_j^* \leq y_L . In writing out the likelihood function, we first define an indicator function I(y_j) where:

I(y_j) = \begin{cases} 0 & \textrm{if} \; y_j = y_L \\ 1 & \textrm{if} \; y_j \neq y_L. \end{cases}

Next, we mean \Phi to be the standard normal cumulative distribution function and \phi to be the standard normal probability density function. For a data set with N observations the likelihood function for a type I Tobit is

\prod _{j=1}^N \left(\frac{\phi \left(\frac{Y_j-X_j\beta }{\sigma }\right)}{\sigma }\right)^{I\left(Y_j\right)} \left(1-\Phi \left(\frac{X_j\beta}{\sigma}\right)\right)^{1-I\left(Y_j\right)}

Etymology

The term “tobit” was derived from Tobin's name and by adding the suffix "-it," as for the 1964 probit model by Arthur Goldberger.[2]

See also

References

  1. ^ McDonald, John F.; Moffit, Robert A. (1980), "The Uses of Tobit Analysis", The Review of Economics and Statistics (The MIT Press) 62 (2): 318–321, http://www.jstor.org/stable/1924766 
  2. ^ International Encyclopedia of the Social Sciences (2008)

Further reading

External links