Gravity's Rainbow: A dynamic latent space model for the world trade network

The gravity model, long the empirical workhorse for modeling international trade, ignores network dependencies in bilateral trade data, instead assuming that dyadic trade is independent, conditional on a hierarchy of covariates over country, time, and dyad. We argue that there are theoretical as wel...

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Bibliographic Details
Published in:Network science (Cambridge University Press) 2013-04, Vol.1 (1), p.95-118
Main Authors: WARD, MICHAEL D., AHLQUIST, JOHN S., ROZENAS, ARTURAS
Format: Article
Language:English
Subjects:
Graphs
Gravity
Heuristic
International trade
Specification
Tracking
Usefulness
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Summary:The gravity model, long the empirical workhorse for modeling international trade, ignores network dependencies in bilateral trade data, instead assuming that dyadic trade is independent, conditional on a hierarchy of covariates over country, time, and dyad. We argue that there are theoretical as well as empirical reasons to expect network dependencies in international trade. Consequently, standard gravity models are empirically inadequate. We combine a gravity model specification with “latent space” networks to develop a dynamic mixture model for real-valued directed graphs. The model simultaneously incorporates network dependencies in both trade incidence and trade volumes. We estimate this model using bilateral trade data from 1990 to 2008. The model substantially outperforms standard accounts in terms of both in- and out-of-sample predictive heuristics. We illustrate the model's usefulness by tracking trading propensities between the USA and China.
ISSN:2050-1242
2050-1250
DOI:10.1017/nws.2013.1