A trade flow forecasting system utilizing the RAS algorithm effectively disaggregates high-level forecasts into detailed commodity classifications using historical data. However, the challenge arises when incorporating the impact of new tariffs without any historical tariff data. The current method involves using historical trade patterns as a base matrix and applying RAS to distribute aggregate forecasts while maintaining these patterns. The system needs to estimate tariff impacts on trade volumes by commodity, incorporate price elasticity of demand, and model substitution effects, also known as trade diversion. The goal is to integrate these elements seamlessly into the existing RAS framework. Suggestions are sought for modeling approaches that can function with limited historical tariff data, with a particular interest in econometric methods and data science techniques that ensure consistency across different levels of aggregation.
Otavio (Tavi) Costa
@TaviCosta
·
Apr 2 The initial estimates suggest that we may have the highest average tariff rates since Smoot-Hawley, which came before the Great Depression.
One notable difference, however:
Today’s equity market is far more overvalued than it was at that time.
Scott Lincicome
@scottlincicome
·
Apr 1 One last reminder before Liberation Day:“Are tariffs bad for growth? Yes, say five decades of data from 150 countries” https://pmc.ncbi.nlm.nih.gov/articles/PMC7255316/#:~:text=Using%20aggregated%20annual%20data%20for,magnitude%20of%20the%20tariff%20change…
Ananth Veluvali
@AnanthVeluvali
·
Apr 6 Figure out how much Trump’s new tariffs might cost you in under 10 seconds: https://tariffeffects.comThis maps key product categories to their main exporters and determines how tariff changes might affect prices (data primarily from http://census.gov and BLS).
Michael McDonough
@M_McDonough
·
Mar 31 WSL POLITICS now includes historical monthly tariff rates by U.S. trading partner, spanning several decades—with additional data coming soon!
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