et_tobit: \insertCiteEaton1995;textualgravity threshold Tobit model (ET...

In gravity: Estimation Methods for Gravity Models

\insertCiteEaton1995;textualgravity threshold Tobit model (ET Tobit)

Description

et_tobit estimates gravity models in their additive form by conducting a left-censored regression.

Usage

et_tobit(dependent_variable, distance, additional_regressors = NULL, data, ...)

Arguments

dependent_variable	(Type: character) name of the dependent variable. Following \insertCiteCarson2007;textualgravity, the smallest positive flow value is used as an estimate of the threshold, this value is is added to the dependent_variable, the result is logged and taken as the dependent variable in the Tobit estimation with lower bound equal to the log of the smallest possible flow value.
distance	(Type: character) name of the distance variable that should be taken as the key independent variable in the estimation. The distance is logged automatically when the function is executed.
additional_regressors	(Type: character) names of the additional regressors to include in the model (e.g. a dummy variable to indicate contiguity). Unilateral metric variables such as GDP should be inserted via the arguments income_origin and income_destination. Write this argument as c(contiguity, common currency, ...). By default this is set to NULL.
data	(Type: data.frame) the dataset to be used.
...	Additional arguments to be passed to the function.

Details

et_tobit represents the \insertCiteEaton1995;textualgravity Tobit model which is often used when several gravity models are compared, instead of adding number 1 to the dependent variable as done in tobit, the constant added to the data is estimated and interpreted as a threshold.

When taking the log of the gravity equation flows equal to zero constitute a problem as their log is not defined. Therefore, a constant is added to the flows.

Compared to the usual ET-Tobit approaches, in this package, the estimation of the threshold is done before the other parameters are estimated.

We follow \insertCiteCarson2007;textualgravity, who show that taking the minimum positive flow value as an estimate of the threshold is super-consistent and that using this threshold estimate ensures that the parameter MLE are asymptotically normal with the asymptotic variance identical to the variance achieved when the threshold is known. Hence, first the threshold is estimated as the minimum positive flow. This threshold is added to the flow variable, it is logged afterwards and taken as the dependent variable.

The Tobit estimation is then conducted using the censReg function and setting the lower bound equal to the log of the minimum positive flow value which was added to all observations.

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.

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.

The function is designed for cross-sectional data, but can be 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.

Value

The function returns the summary of the estimated gravity model as a censReg-object.

References

For more information on gravity models, theoretical foundations and estimation methods in general see

\insertRef

Anderson1979gravity

\insertRef

Anderson2001gravity

\insertRef

Anderson2010gravity

\insertRef

Baier2009gravity

\insertRef

Baier2010gravity

\insertRef

Feenstra2002gravity

\insertRef

Head2010gravity

\insertRef

Head2014gravity

\insertRef

Santos2006gravity

and the citations therein.

See Gravity Equations: Workhorse, Toolkit, and Cookbook for gravity datasets and Stata code for estimating gravity models.

For estimating gravity equations using panel data see

\insertRef

Egger2003gravity

\insertRef

Gomez-Herrera2013gravity

and the references therein.

See Also

censReg, et_tobit

Examples

# Example for CRAN checks:
# Executable in < 5 sec
library(dplyr)
data("gravity_no_zeros")

# Choose 5 countries for testing
countries_chosen <- c("AUS", "CHN", "GBR", "BRA", "CAN")
grav_small <- filter(gravity_no_zeros, iso_o %in% countries_chosen)

grav_small <- grav_small %>%
  mutate(
    flow = ifelse(flow < 5, 0, flow), # cutoff for testing purposes
    lgdp_o = log(gdp_o),
    lgdp_d = log(gdp_d)
  )

fit <- et_tobit(
  dependent_variable = "flow",
  distance = "distw",
  additional_regressors = c("rta", "lgdp_o", "lgdp_d"),
  data = grav_small
)

gravity documentation built on May 2, 2023, 9:13 a.m.