BVW estimates gravity models via Bonus
vetus OLS with GDP-weights.
BVW(y, dist, x, inc_d, inc_o, vce_robust = TRUE, data, ...)name (type: character) of the dependent variable in the dataset
data, e.g. trade flows. This dependent variable is divided by the
product of unilateral incomes (inc_o and inc_d, e.g.
GDPs or GNPs of the countries of interest) and logged afterwards.
The transformed variable is then used as the dependent variable in the
estimation.
name (type: character) of the distance variable in the dataset
data containing a measure of distance between all pairs of bilateral
partners. It is logged automatically when the function is executed.
vector of names (type: character) of those bilateral variables in
the dataset data that should be taken as the independent variables
in the estimation. If an independent variable is a dummy variable,
it should be of type numeric (0/1) in the dataset. If an independent variable
is defined as a ratio, it should be logged. Unilateral metric variables
such as GDPs should be inserted via the arguments inc_o
for the country of origin and inc_d for the country of destination.
As country specific effects are subdued due to demeaning, no further
unilateral variables apart from inc_o and inc_d can be
added.
variable name (type: character) of the income of the country of
destination in the dataset data. The dependent variable y is
divided by the product of the incomes inc_d and inc_o.
variable name (type: character) of the income of the country of
origin in the dataset data. The dependent variable y is
divided by the product of the incomes inc_d and inc_o.
robust (type: logic) determines whether a robust
variance-covariance matrix should be used. The default is set to TRUE.
If set TRUE the estimation results equal the Stata results for
robust estimation.
name of the dataset to be used (type: character).
To estimate gravity equations, a square gravity dataset including bilateral
flows defined by the argument y, ISO-codes of type character
(called iso_o for the country of origin and iso_d for the
destination country), a distance measure defined by the argument dist
and other potential influences given as a vector in x are required.
All dummy variables should be of type numeric (0/1). Missing trade flows as
well as incomplete rows should be excluded from the dataset.
Furthermore, flows equal to zero should be excluded as the gravity equation
is estimated in its additive form.
As, to our knowledge at the moment, there is no explicit literature covering
the estimation of a gravity equation by BVW
using panel data, cross-sectional data should be used.
additional arguments to be passed to BVW.
The function returns the summary of the estimated gravity model as an
lm-object.
Bonus vetus OLS is an estimation method for gravity models
developed by Baier and Bergstrand (2009, 2010) using GDP-weights to center a
Taylor-series (see the references for more information).
To execute the function a square gravity dataset with all pairs of
countries, ISO-codes for the country of origin and destination, a measure of
distance between the bilateral partners as well as all
information that should be considered as dependent an independent
variables is needed.
Make sure the ISO-codes are of type "character".
Missing bilateral flows as well as incomplete rows should be
excluded from the dataset.
Furthermore, flows equal to zero should be excluded as the gravity equation
is estimated in its additive form.
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
the website
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.
For estimating gravity equations via Bonus Vetus OLS see
Baier, S. L. and Bergstrand, J. H. (2009) <DOI:10.1016/j.jinteco.2008.10.004>
Baier, S. L. and Bergstrand, J. H. (2010) in Van Bergeijk, P. A., & Brakman, S. (Eds.) (2010) chapter 4 <DOI:10.1111/j.1467-9396.2011.01000.x>
For more information on gravity models, theoretical foundations and estimation methods in general see
Anderson, J. E. (1979) <DOI:10.12691/wjssh-2-2-5>
Anderson, J. E. (2010) <DOI:10.3386/w16576>
Anderson, J. E. and van Wincoop, E. (2003) <DOI:10.3386/w8079>
Head, K., Mayer, T., & Ries, J. (2010) <DOI:10.1016/j.jinteco.2010.01.002>
Head, K. and Mayer, T. (2014) <DOI:10.1016/B978-0-444-54314-1.00003-3>
Santos-Silva, J. M. C. and Tenreyro, S. (2006) <DOI:10.1162/rest.88.4.641>
and the citations therein.
See Gravity Equations: Workhorse, Toolkit, and Cookbook for gravity datasets and Stata code for estimating gravity models.
# NOT RUN {
data(Gravity)
BVW(y="flow", dist="distw", x=c("rta"), inc_o="gdp_o", inc_d="gdp_d",
vce_robust=TRUE, data=Gravity)
BVW(y="flow", dist="distw", x=c("rta", "comcur", "contig"),
inc_o="gdp_o", inc_d="gdp_d", vce_robust=FALSE, data=Gravity)
# }
# NOT RUN {
# }
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