sils: Structural Iterated Least Squares (SILS)

Description

sils estimates gravity models via Structural Iterated Least Squares and an explicit inclusion of the Multilateral Resistance terms.

Usage

sils(dependent_variable, distance, additional_regressors = NULL,
  income_origin, income_destination, code_origin, code_destination,
  maxloop = 100, decimal_places = 4, robust = FALSE,
  verbose = FALSE, data, ...)

Arguments

dependent_variable

(Type: character) name of the dependent variable. This dependent variable is divided by the product of unilateral incomes such (i.e. income_origin and income_destination) and logged afterwards.

distance

(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.

additional_regressors

(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 income_origin and income_destination. As country specific effects are subdued due to demeaning, no further unilateral variables apart from incomes can be added.

Write this argument as c(contiguity, common currency, ...). By default this is set to NULL.

income_origin

(Type: character) origin income variable (e.g. GDP) in the dataset.

income_destination

(Type: character) destination income variable (e.g. GDP) in the dataset.

code_origin

(Type: character) country of origin variable (e.g. ISO-3 country codes). The variables are grouped using this parameter.

code_destination

(Type: character) country of destination variable (e.g. country ISO-3 codes). The variables are grouped using this parameter.

maxloop

(Type: numeric) maximum number of outer loop iterations. The default is set to 100. There will be a warning if the iterations did not converge.

decimal_places

(Type: numeric) number of decimal places that should not change after a new iteration for the estimation to stop. The default is set to 4.

robust

(Type: logical) whether robust fitting should be used. By default this is set to FALSE.

verbose

(Type: logical) determines whether the estimated coefficients of each iteration should be printed in the console. The default is set to FALSE.

data

(Type: data.frame) the dataset to be used.

...

Additional arguments to be passed to the function.

Value

The function returns the summary of the estimated gravity model as an lm-object. It furthermore returns the resulting coefficients for each iteration.

Details

sils is an estimation method for gravity models developed by Head2014;textualgravity.

The function sils utilizes the relationship between the Multilateral Resistance terms and the transaction costs. The parameters are estimated by an iterative procedure. The function executes loops until the parameters stop changing significantly.

sils 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 sils using panel data, and we do not recommend to apply this method in this case.

References

For more information on gravity models, theoretical foundations and estimation methods in general see

Anderson1979gravity

Anderson2001gravity

Anderson2010gravity

Baier2009gravity

Baier2010gravity

Feenstra2002gravity

Head2010gravity

Head2014gravity

Santos2006gravity

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

Egger2003gravity

Gomez-Herrera2013gravity

and the references therein.

Examples

Run this code

# NOT RUN {
# 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 <- sils(
  dependent_variable = "flow",
  distance = "distw",
  additional_regressors = "rta",
  income_origin = "gdp_o",
  income_destination = "gdp_d",
  code_origin = "iso_o",
  code_destination = "iso_d",
  maxloop = 50,
  dec_places = 3,
  robust = FALSE,
  verbose = FALSE,
  data = grav_small
)
# }

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