DDM: Double Demeaning, DDM
In jpburgard/gravity: Estimation Methods for Gravity Models

Description Usage Arguments Details Value References See Also Examples

DDM estimates gravity models via double demeaning the left hand side and right hand side of the gravity equation.

1	DDM(y, dist, x, vce_robust = TRUE, data, ...)

`y`	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.
`dist`	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.
`x`	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 effects drop out due to double demeaning and therefore cannot be estimated.
`vce_robust`	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 are consistent with the Stata code provided at the website Gravity Equations: Workhorse, Toolkit, and Cookbook when choosing robust estimation.
`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 `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 `DDM` using panel data, cross-sectional data should be used.
`...`	additional arguments to be passed to `DDM`.

DDM is an estimation method for gravity models presented in Head and Mayer (2014) (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. Country specific effects are subdued due double demeaning. Hence, unilateral income proxies such as GDP cannot be considered as exogenous variables.

DDM 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 DDM 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 Double Demeaning as well as information on gravity models, theoretical foundations and estimation methods in general see

Head, K. and Mayer, T. (2014) <DOI:10.1016/B978-0-444-54314-1.00003-3>

as well as

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>

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.

lm, coeftest, vcovHC

## Not run: 
data(Gravity_no_zeros)

DDM(y="flow", dist="distw", x=c("rta"), 
vce_robust=TRUE, data=Gravity_no_zeros)

DDM(y="flow", dist="distw", x=c("rta", "comcur", "contig"), 
vce_robust=TRUE, data=Gravity_no_zeros)

## End(Not run)