ddm estimates gravity models via double demeaning the
left hand side and right hand side of the gravity equation.
ddm(dependent_variable, regressors, codes, robust = TRUE, data, ...)name (type: character) of the dependent variable in the dataset
data (e.g. trade flows).
This variable is logged and then used as the dependent variable in the estimation.
name (type: character) of the regressors to include in the model.
Include the distance variable in the dataset data containing a measure of
distance between all pairs of bilateral partners and bilateral variables that should
be taken as the independent variables in the estimation.
Write this argument as c(distance, contiguity, common curreny, ...).
variable name (type: character) of the code of the country
of origin and destination (e.g. ISO-3 codes from the variables iso_o and iso_d) in the
example datasets).
The variables are grouped by using iso_o and iso_d to obtain estimates.
Write this argument as c(code origin, code destination).
robust (type: logical) determines whether a robust
variance-covariance matrix should be used. By default is set to TRUE.
If robust = TRUE the estimation results are consistent with the
Stata code provided at Gravity Equations: Workhorse, Toolkit, and Cookbook
when choosing robust estimation.
name of the dataset to be used (type: character).
To estimate gravity equations you need a square dataset including bilateral
flows defined by the argument dependent_variable, ISO codes or similar of type character
(e.g. iso_o for the country of origin and iso_d for the
destination country), a distance measure defined by the argument distance
and other potential influences (e.g. contiguity and common currency) given as a vector in
regressors are required.
All dummy variables should be of type numeric (0/1).
Make sure the ISO codes are of type "character".
If an independent variable is defined as a ratio, it should be logged.
The user should perform some data cleaning beforehand to remove observations that contain entries that can distort estimates.
The function will remove zero flows and distances.
additional arguments to be passed to ddm.
The function returns the summary of the estimated gravity model as an
lm-object.
ddm is an estimation method for gravity models presented
in Head2014;textualgravity.
Country specific effects are subdued due double demeaning. Hence, unilateral income proxies such as GDP cannot be considered as exogenous variables.
Unilateral effect drop out due to double demeaning and therefore cannot be estimated.
ddm 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 ddm using panel data,
we do not recommend to apply this method in this case.
For more information on Double Demeaning as well as information on gravity models, theoretical foundations and estimation methods in general see
Head2014gravity
as well as
Anderson1979gravity
Anderson2001gravity
Anderson2010gravity
Baier2009gravity
Baier2010gravity
Head2010gravity
Santos2006gravity
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_no_zeros)
ddm(dependent_variable = "flow", regressors = c("distw", "rta"),
codes = c("iso_o", "iso_d"),
robust = TRUE, data = gravity_no_zeros)
ddm(dependent_variable = "flow", regressors = c("distw", "rta", "comcur", "contig"),
codes = c("iso_o", "iso_d"),
robust=TRUE, data=gravity_no_zeros)
# }
# NOT RUN {
# }
# NOT RUN {
# }
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