tobit
represents the left-censored tobit Tobin1958;textualgravity
approach utilizing a known censoring threshold
which is often used when several gravity models are compared.
When taking the log of the gravity equation flows equal to zero
constitute a problem as their log is not defined.
Therefore, in the execution of the function the number 1
is added to all flows and the log(flows+1)
is
taken as the dependent variable.
The tobit estimation is conducted using the censReg
function and setting the lower bound equal to 0
as
log(1)=0
represents the smallest flows in the transformed
variable.
A tobit regression represents a combination of a binary and a
linear regression.
This procedure has to be taken into consideration when
interpreting the estimated coefficients.
The marginal effects of an explanatory variable on the expected value of
the dependent variable equals the product of both the probability of the
latent variable exceeding the threshold and the marginal effect of the
explanatory variable of the expected value of the latent variable.
The function is designed for cross-sectional data,
but can be easily extended to panel data using the
censReg
function.
A robust estimations is not implemented to the present
as the censReg
function is not
compatible with the vcovHC
function.
For a more elaborate Tobit function, see ek_tobit
for the Eaton and Kortum (2001) Tobit model where each zero trade volume
is assigned a country specific interval with the upper
bound equal to the minimum positive trade level of the respective
importing country.