TITLE:
A Bayesian Vector Autoregression Model to Estimate Bilateral Trade Flows in Panel Data Setting
AUTHORS:
Prabuddha Sanyal, Mark Ehlen
KEYWORDS:
Bilateral Trade, Bayesian Analysis, Vector Autoregression
JOURNAL NAME:
Modern Economy,
Vol.16 No.7,
July
24,
2025
ABSTRACT: This study examines how trade shocks that start in one large economy ripple through other countries and how long those effects stick around. Using quarterly bilateral-trade data for 2012-2023, the authors estimate a Bayesian four-country VAR model (China, Germany, Japan, United States) identified by sign restrictions. Impulse-response functions show that a one-standard-deviation drop in Chinese exports cuts German and Japanese exports by about three percent on impact, whereas German shocks are one-third as large, Japanese shocks quickly rebound, and U.S. shocks barely travel abroad. Forecast-error variance decomposition at a ten-quarter horizon confirms China’s central role: its shocks explain roughly one-third of the medium-run export volatility in Germany and Japan, while 90 percent of U.S. volatility remains home-grown. Persistence analysis finds that own shocks in China, Germany, and Japan stay near full strength for five years, whereas U.S. shocks fade faster.