BVW: Bonus vetus OLS, BVW
In jpburgard/gravity: Estimation Methods for Gravity Models

Description Usage Arguments Details Value References See Also Examples

BVW estimates gravity models via Bonus vetus OLS with GDP-weights.

1	BVW(y, dist, x, inc_o, inc_d, vce_robust = TRUE, data, ...)

`y`	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 (named `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.
`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 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.
`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`.
`inc_d`	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`.
`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 `BVW` using panel data, cross-sectional data should be used.
`...`	additional arguments to be passed to `BVW`.

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.

The function returns the summary of the estimated gravity model as an lm-object.

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.

lm, coeftest, vcovHC

## Not run: 
data(Gravity_no_zeros)

BVW(y="flow", dist="distw", x=c("rta"), 
inc_o="gdp_o", inc_d="gdp_d", vce_robust=TRUE, data=Gravity_no_zeros)

BVW(y="flow", dist="distw", x=c("rta", "comcur", "contig"), 
inc_o="gdp_o", inc_d="gdp_d", vce_robust=TRUE, data=Gravity_no_zeros)

## End(Not run)