Description Usage Arguments Details Value References See Also Examples

`nbpml`

estimates gravity models in their
multiplicative form via Negative Binomial Pseudo Maximum Likelihood.

1 2 |

`dependent_variable` |
(Type: character) name of the dependent variable. This variable is logged and then used as the dependent variable in the estimation. |

`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 GDPs can be added but those variables have to be logged first. Interaction terms can be added. Write this argument as |

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

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

`...` |
Additional arguments to be passed to the function. |

`nbpml`

is an estimation method for gravity models
belonging to generalized linear models. It is estimated via `glm.nb`

using the negative binomial distribution and a log-link.

For similar functions, utilizing the multiplicative form via the log-link,
but different distributions, see `nbpml`

, `gpml`

,
and `nls`

.

`gpml`

estimation can be used for both, cross-sectional as well as
panel data, but its 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 \insertCiteEgger2003;textualgravity, \insertCiteGomez-Herrera2013;textualgravity and \insertCiteHead2010;textualgravity.

The function returns the summary of the estimated gravity model similar to a
`glm`

-object.

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

\insertRefAnderson1979gravity

\insertRefAnderson2001gravity

\insertRefAnderson2010gravity

\insertRefBaier2009gravity

\insertRefBaier2010gravity

\insertRefFeenstra2002gravity

\insertRefHead2010gravity

\insertRefHead2014gravity

\insertRefSantos2006gravity

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

\insertRefEgger2003gravity

\insertRefGomez-Herrera2013gravity

and the references therein.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# 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 <- nbpml(
dependent_variable = "flow",
distance = "distw",
additional_regressors = c("rta", "iso_o", "iso_d"),
data = grav_small
)
``` |

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