Forecasting India’s foreign trade dynamics: evaluation of alternative forecasting models in the post-pandemic period

A. Mansurali; Sarbjit Singh Oberoi; P. Mary Jeyanthi

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Need to know

Accurate forecasting of a country’s key economic variables, such as GDP, trade, and inflation is vital in a globalized world.
Given trade’s importance in India’s GDP, forecasts of exports and imports are essential in determining trade deficit and surplus with other nations and framing policies to expand the economy.
Our research applies simple time series models to complex machine learning algorithms to determine the best model to forecast the value of merchandise traded.
On building models and analysing them for best forecasting, the Multi- Layer Perceptron Neural Network model was found to perform best.

Abstract

According to an estimate by the International Monetary Fund, India’s gross domestic product was expected to reach US$3.82 trillion in the 2023/4 financial year. At the time of writing, India is the fifth-largest economy in the world, and one of the fastest growing. In 2018 India’s share in global trade (in both merchandise and services) was 2.1% for exports (US$481.74 billion out of a total of US$23 044 billion) and 2.6% for imports (US$600.62 billion out of a total of US$23 112 billion). Starting from 2016/17, exports from India grew constantly for almost three years, and total exports peaked at more than US$0.5 trillion for the first time in 2018/19. This study aims to determine whether India’s foreign trade will grow or diminish in the period following the Covid-19 pandemic (2020–23) and during the ongoing Ukraine crisis. We use various quantitative and qualitative techniques to predict the growth in foreign trade. Comparing several available forecasting models, we identify the best for forecasting the value of India’s trade in future.

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