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K-fold Cross-Validation and the Gravity Model of Bilateral Trade

机译：K折交叉验证与双边贸易引力模型

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摘要

This paper contributes to the gravity model literature by giving a side-by-side comparison of in-sample and out-of-sample data techniques, specifically k-fold cross-validation, to show the benefits of using out-of-sample data techniques when examining the gravity model of bilateral trade. This shifts the focus from sample uncertainty, which is limited within bilateral trade data, to model uncertainty, which poses a larger potential problem in this context (Varian, Journal of Economic Perspectives 28: 3-28, 2014). This research also begins addressing the implicit regularities that are often imposed upon the variables within the gravity model by examining possible interaction terms and various model specifications using the aforementioned k-fold cross-validation technique. The results indicate that the k-fold cross-validation method provides more robust models and prevents over-fitting the model with practically and statistically insignificant variables. Moreover, it finds strong evidence to suggest that the log specification of GDP and GDP per capita in the gravity model needs to consider raised powers of the variables in order to give the best predictive model and help avoid omitted variable bias. This change reduces the expected increases in bilateral trade of a currency union by almost 50 %, suggesting a large previously omitted variable bias within the model. Similar biases are revealed in the coefficient estimates for regional trade agreements and generalized system of preferences.
机译：本文通过对样本内和样本外数据技术（特别是k倍交叉验证）进行并排比较，为重力模型文献做出了贡献，以显示使用样本外数据的好处检查双边贸易引力模型时的技巧。这将焦点从样本不确定性（限制在双边贸易数据中）转移到模型不确定性，在这种情况下，模型不确定性提出了更大的潜在问题（Varian，Journal of Economic Perspectives 28：3-28，2014）。这项研究还通过使用前面提到的k倍交叉验证技术检查可能的相互作用项和各种模型规范，来解决通常对重力模型中的变量施加的隐式规则。结果表明，k折交叉验证方法提供了更健壮的模型，并防止了模型在实际和统计上无关紧要的变量过度拟合。此外，它发现有力的证据表明，重力模型中GDP和人均GDP的对数规格需要考虑变量的提高能力，以便提供最佳预测模型并帮助避免遗漏变量偏差。这种变化使货币联盟双边贸易的预期增长减少了近50％，这表明该模型中以前存在较大的可变偏差。区域贸易协定和普遍优惠制的系数估计中也显示出类似的偏差。

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