<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael D. Ward</style></author><author><style face="normal" font="default" size="100%">John S. Ahlquist</style></author><author><style face="normal" font="default" size="100%">Arturas Rozenas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gravity's Rainbow: A Dynamic Latent Space Model for the World Trade Network</style></title><secondary-title><style face="normal" font="default" size="100%">Network Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">methodology</style></keyword><keyword><style  face="normal" font="default" size="100%">networks</style></keyword><keyword><style  face="normal" font="default" size="100%">trade</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2012</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">tbd</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">&lt;p&gt;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. &amp;nbsp; We argue that there are theoretical reasons as well as empirical reasons to expect network dependencies in international trade. Consequently standard gravity models are empirically inadequate. &amp;nbsp;We combine a gravity model specification with ``latent space&amp;#39;&amp;#39; networks to develop a dynamic mixture model for real-valued directed graphs. &amp;nbsp;The model incorporates network dependencies in both trade incidence and trade volumes at both levels simultaneously. &amp;nbsp;We estimate this model using bilateral trade data from 1990-2008. &amp;nbsp;The model substantially outperforms standard accounts in terms of both in- and out-of-sample predictive heuristics. &amp;nbsp;We illustrate the model&amp;#39;s usefulness by tracking trading propensities between the USA and China.&lt;/p&gt;
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