A New Approach to Overcoming Zero Trade in Gravity Models to Avoid Indefinite Values in Linear Logarithmic Equations and Parameter Verification Using Machine Learning

TL;DR

This paper introduces a two-step method combined with machine learning to address zero trade flows in gravity models, enabling more accurate parameter estimation without indefinite values.

Contribution

It proposes a novel two-step approach with machine learning for gravity model parameter estimation in the presence of zero trade flows, improving upon existing methods.

Findings

GDP and distance powers are in the same cluster, both approximately one

The method effectively handles zero trade flows in gravity models

Applicable to other log-linear regression problems

Abstract

The presence of a high number of zero flow trades continues to provide a challenge in identifying gravity parameters to explain international trade using the gravity model. Linear regression with a logarithmic linear equation encounters an indefinite value on the logarithmic trade. Although several approaches to solving this problem have been proposed, the majority of them are no longer based on linear regression, making the process of finding solutions more complex. In this work, we suggest a two-step technique for determining the gravity parameters: first, perform linear regression locally to establish a dummy value to substitute trade flow zero, and then estimating the gravity parameters. Iterative techniques are used to determine the optimum parameters. Machine learning is used to test the estimated parameters by analyzing their position in the cluster. We calculated international trade figures for 2004, 2009, 2014, and 2019. We just examine the classic gravity equation and discover that the powers of GDP and distance are in the same cluster and are both worth roughly one. The strategy presented here can be used to solve other problems involving log-linear regression.