Analysis of Bilateral Trade Flow and Machine Learning Algorithms for GDP Forecasting

J. Sun, Y. Suo, S. Park, T. Xu, Y. Liu, W. Wang

Abstract


The terms imports and exports describe goods and services traded between countries. Countries import goods they cannot produce domestically or can obtain at a lower cost from another country. According to the World Trade Organization (WTO) reports, the U.S. is the world’s largest importer based on capital investment, followed by the E.U., China, Germany, and Japan. For exports, China leads the world with an official trade amount of $1.904 trillion in 2013. E.U. ranks second, followed by U.S., Germany, and Japan. Trade in goods and services is defined as a change in ownership of material resources and services between economies. Trade indicators include the sale of goods and services as well as barter transactions or goods exchanged and are measured in million USD, the percentage of GDP for net trade, and the annual export and import growth. This study analyzes imports and exports of all countries for the 1960-2017 period and evaluates the correlations in trade statistics to predict future imports and exports. Since the GDP for any country depends mainly on trade, this study examines trade data and compares various machine learning algorithms to forecast a country’s GDP.


Keywords


imports; exports; GDP; trade statistics; GDP forecast

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