bvw | R Documentation |
bvw
estimates gravity models via Bonus
vetus OLS with income-weights.
bvw(
dependent_variable,
distance,
additional_regressors = NULL,
income_origin,
income_destination,
code_origin,
code_destination,
robust = FALSE,
data,
...
)
dependent_variable |
(Type: character) name of the dependent variable. This dependent variable is
divided by the product of unilateral incomes such (i.e. |
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
Write this argument as |
income_origin |
(Type: character) origin income variable (e.g. GDP) in the dataset. |
income_destination |
(Type: character) destination income variable (e.g. GDP) in the dataset. |
code_origin |
(Type: character) country of origin variable (e.g. ISO-3 country codes). The variables are grouped using this parameter. |
code_destination |
(Type: character) country of destination variable (e.g. country ISO-3 codes). The variables are grouped using this parameter. |
robust |
(Type: logical) whether robust fitting should be used. By default this is set to |
data |
(Type: data.frame) the dataset to be used. |
... |
Additional arguments to be passed to the function. |
Bonus vetus OLS is an estimation method for gravity models developed by \insertCiteBaier2009,Baier2010;textualgravity using income-weights to center a Taylor series.
The bvw
function considers Multilateral Resistance terms and allows to
conduct comparative statics. Country specific effects are subdued due
to demeaning. Hence, unilateral variables apart from inc_o
and inc_d
cannot be included in the estimation.
bvw
is designed to be consistent with the Stata code provided at
Gravity Equations: Workhorse, Toolkit, and Cookbook
when choosing robust estimation.
As, to our knowledge at the moment, there is no explicit literature covering
the estimation of a gravity equation by bvw
using panel data,
we do not recommend to apply this method in this case.
The function returns the summary of the estimated gravity model as an
lm
-object.
For more information on gravity models, theoretical foundations and estimation methods in general see
\insertRefAnderson1979gravity
\insertRefAnderson2001gravity
\insertRefAnderson2010gravity
\insertRefBaier2009gravity
\insertRefBaier2010gravity
\insertRefFeenstra2002gravity
\insertRefHead2010gravity
\insertRefHead2014gravity
\insertRefSantos2006gravity
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
\insertRefEgger2003gravity
\insertRefGomez-Herrera2013gravity
and the references therein.
lm
, coeftest
,
vcovHC
# 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)
fit <- bvw(
dependent_variable = "flow",
distance = "distw",
additional_regressors = c("rta", "comcur", "contig"),
income_origin = "gdp_o",
income_destination = "gdp_d",
code_origin = "iso_o",
code_destination = "iso_d",
robust = FALSE,
data = grav_small
)
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