Description Usage Arguments Details Value References See Also Examples
GPML
estimates gravity models in their
multiplicative form via Gamma Pseudo Maximum Likelihood.
1 |
y |
name (type: character) of the dependent variable in the dataset
|
dist |
name (type: character) of the distance variable in the dataset
|
x |
vector of names (type: character) of those bilateral variables in
the dataset |
vce_robust |
robust (type: logic) determines whether a robust
variance-covariance matrix should be used. The default is set to |
data |
name of the dataset to be used (type: character).
To estimate gravity equations, a square gravity dataset including bilateral
flows defined by the argument |
... |
additional arguments to be passed to functions used by
|
GPML
is an estimation method for gravity models
belonging to generalized linear models.
It is estimated via glm2
using the gamma distribution and a log-link.
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.
Flows equal to zero have to be excluded as the gamma distribution is used.
For similar functions, utilizing the multiplicative form via the log-link,
but different distributions, see PPML
, NLS
, and NBPML
.
GPML
estimation can be used for both, cross-sectional as well as
panel data.
It is up to the user to ensure that the functions can be applied
to panel data.
Depending on the panel dataset and the variables -
specifically the type of fixed effects -
included in the model, it may easily occur that the model is not computable.
Also, note that by including bilateral fixed effects such as country-pair
effects, the coefficients of time-invariant observables such as distance
can no longer be estimated.
Depending on the specific model, the code of the
respective function may has to be changed in order to exclude the distance
variable from the estimation.
At the very least, the user should take special
care with respect to the meaning of the estimated coefficients and variances
as well as the decision about which effects to include in the estimation.
When using panel data, the parameter and variance estimation of the models
may have to be changed accordingly.
For a comprehensive overview of gravity models for panel data
see Egger and Pfaffermayr (2003), Gomez-Herrera (2013) and Head, Mayer and
Ries (2010) as well as the references therein.
The function returns the summary of the estimated gravity model similar to a
glm
-object.
For more information on the estimation of gravity equations via Gamma Pseudo maximum Likelihood see
Santos-Silva, J. M. C. and Tenreyro, S. (2006) <DOI:10.1162/rest.88.4.641>
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>
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>
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>
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
Egger, P., & Pfaffermayr, M. (2003) <DOI:10.1007/s001810200146>
Gomez-Herrera, E. (2013) <DOI:10.1007/s00181-012-0576-2>
and the references therein.
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Not run:
data(Gravity_no_zeros)
Gravity_no_zeros$lgdp_o <- log(Gravity_no_zeros$gdp_o)
Gravity_no_zeros$lgdp_d <- log(Gravity_no_zeros$gdp_d)
GPML(y="flow", dist="distw", x=c("rta", "lgdp_o", "lgdp_d"),
vce_robust=TRUE, data=Gravity_no_zeros)
GPML(y="flow", dist="distw", x=c("rta", "iso_o", "iso_d"),
vce_robust=TRUE, data=Gravity_no_zeros)
## End(Not run)
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