tetrads estimates gravity models
by taking the ratio of the ratio of flows.
tetrads(dependent_variable, distance, additional_regressors, code_origin, code_destination, filter_origin = NULL, filter_destination = NULL, multiway = FALSE, data, ...)
(Type: character) name of the dependent variable. This variable is logged and then used as the dependent variable in the estimation.
(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.
(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
(Type: character) country of origin variable (e.g. ISO-3 country codes). The variables are grouped using this parameter.
(Type: character) country of destination variable (e.g. country ISO-3 codes). The variables are grouped using this parameter.
(Type: character) Reference exporting country.
(Type: character) Reference importing country.
(Type: logical) in case
(Type: data.frame) the dataset to be used.
Additional arguments to be passed to the function.
The function returns the summary of the estimated gravity model as an
tetrads is an estimation method for gravity models
developed by Head et al. (2010)
tetrads utilizes the multiplicative form of the
gravity equation. After choosing a reference exporter
B one can eliminate importer and exporter fixed effects
by taking the ratio of ratios.
Only those exporters trading with the reference importer and importers trading with the reference exporter will remain for the estimation. Therefore, reference countries should preferably be countries which trade with every other country in the dataset.
After restricting the data in this way,
tetrads estimates the gravity
equation in its additive form by OLS.
By taking the ratio of ratios, all monadic effects diminish, hence no unilateral variables such as GDP can be included as independent variables.
tetrads estimation can be used for both, cross-sectional as well as
panel data. Nonetheless, the function is designed to be consistent with the
Stata code for cross-sectional data provided on the website
Gravity Equations: Workhorse, Toolkit, and Cookbook
when choosing robust estimation.
tetrads was therefore tested for cross-sectional data.
If tetrads is used for panel data, the user may have to drop distance as an independent variable as time-invariant effects drop.
tetrads to panel data see Head et al. (2010)
For more information on gravity models, theoretical foundations and estimation methods in general see
Anderson JE (1979). “A Theoretical Foundation for the Gravity Equation.” The American Economic Review, 69(1), 106--116. ISSN 00028282.
Anderson JE, van Wincoop E (2001). “Gravity with Gravitas: A Solution to the Border Puzzle.” Technical Report 8079, National Bureau of Economic Research. doi: 10.3386/w8079 .
Anderson JE (2010). “The Gravity Model.” Technical Report 16576, National Bureau of Economic Research. doi: 10.3386/w16576 .
Baier SL, Bergstrand JH (2009). “Bonus vetus OLS: A simple method for approximating international trade-cost effects using the gravity equation.” Journal of International Economics, 77(1), 77 - 85. ISSN 0022-1996, doi: 10.1016/j.jinteco.2008.10.004 .
Baier SL, Bergstrand JH (2010). “The Gravity Model in International Trade: Advances and Applications.” In van Bergeijk PAG, Brakman S (eds.), chapter 4. Cambridge University Press. doi: 10.1017/CBO9780511762109 .
Feenstra RC (2002). “Border effects and the gravity equation: consistent methods for estimation.” Scottish Journal of Political Economy, 49(5), 491--506.
Head K, Mayer T, Ries J (2010). “The erosion of colonial trade linkages after independence.” Journal of International Economics, 81(1), 1 - 14. ISSN 0022-1996, doi: 10.1016/j.jinteco.2010.01.002 .
Head K, Mayer T (2014). “Chapter 3 - Gravity Equations: Workhorse,Toolkit, and Cookbook.” In Gopinath G, Helpman E, Rogoff K (eds.), Handbook of International Economics, volume 4 of Handbook of International Economics, 131 - 195. Elsevier. doi: 10.1016/B978-0-444-54314-1.00003-3 .
Silva JMCS, Tenreyro S (2006). “The Log of Gravity.” The Review of Economics and Statistics, 88(4), 641-658. 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.
For estimating gravity equations using panel data see
Egger P, Pfaffermayr M (2003). “The proper panel econometric specification of the gravity equation: A three-way model with bilateral interaction effects.” Empirical Economics, 28(3), 571--580. ISSN 1435-8921, doi: 10.1007/s001810200146 .
Gómez-Herrera E (2013). “Comparing alternative methods to estimate gravity models of bilateral trade.” Empirical Economics, 44(3), 1087--1111. ISSN 1435-8921, doi: 10.1007/s00181-012-0576-2 .
and the references therein.
# 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 <- tetrads( dependent_variable = "flow", distance = "distw", additional_regressors = "rta", code_origin = "iso_o", code_destination = "iso_d", filter_origin = countries_chosen, filter_destination = countries_chosen, data = grav_small )