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The presence of a high number of zero flow trades continues to provide achallenge in identifying gravity parameters to explain international tradeusing the gravity model. Linear regression with a logarithmic linear equationencounters an indefinite value on the logarithmic trade. Although severalapproaches to solving this problem have been proposed, the majority of them areno longer based on linear regression, making the process of finding solutionsmore complex. In this work, we suggest a two-step technique for determining thegravity parameters: first, perform linear regression locally to establish adummy value to substitute trade flow zero, and then estimating the gravityparameters. Iterative techniques are used to determine the optimum parameters.Machine learning is used to test the estimated parameters by analyzing theirposition in the cluster. We calculated international trade figures for 2004,2009, 2014, and 2019. We just examine the classic gravity equation and discoverthat the powers of GDP and distance are in the same cluster and are both worthroughly one. The strategy presented here can be used to solve other problemsinvolving log-linear regression.

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