International trade policies have recently garnered attention for limiting
cross-border exchange of essential goods (e.g. steel, aluminum, soybeans, and
beef). Since trade critically affects employment and wages, predicting future
patterns of trade is a high-priority for policy makers around the world. While
traditional economic models aim to be reliable predictors, we consider the
possibility that Machine Learning (ML) techniques allow for better predictions
to inform policy decisions. Open-government data provide the fuel to power the
algorithms that can explain and forecast trade flows to inform policies. Data
collected in this article describe international trade transactions and
commonly associated economic factors. Machine learning (ML) models deployed
include: ARIMA, GBoosting, XGBoosting, and LightGBM for predicting future trade
patterns, and K-Means clustering of countries according to economic factors.
Unlike short-term and subjective (straight-line) projections and medium-term
(aggre-gated) projections, ML methods provide a range of data-driven and
interpretable projections for individual commodities. Models, their results,
and policies are introduced and evaluated for prediction quality.

本研究介绍了使用机器学习算法，基于国际贸易交易和相关经济因素，进行预测和解释交易流的可能性，并对预测结果和政策决策进行了评估。

机器学习在国际贸易趋势预测中的应用

Application of Machine Learning in Forecasting International Trade  Trends

We investigate the role of networks of alliances in preventing (multilateral)
interstate wars. We first show that, in the absence of international trade, no
network of alliances is peaceful and stable. We then show that international
trade induces peaceful and stable networks: trade increases the density of
alliances so that countries are less vulnerable to attack and also reduces
countries' incentives to attack an ally. We present historical data on wars and
trade, noting that the dramatic drop in interstate wars since 1950, and
accompanying densification and stabilization of alliances, are consistent with
the model but not other prominent theories.

本文研究联盟在防止（多边）国家间战争中的作用，并表明国际贸易会增加联盟的密度，从而减少攻击的可能性和稳定联盟。通过历史数据对战争和贸易进行分析，显示联盟密度增加和稳定与战争数量下降一致，这一结论与其他著名理论不同。