Utilizing various statistical methods to model the impact of the COVID-19 pandemic on Gross domestic product

Abstract

Gross Domestic Product (GDP) is one of the key macroeconomic aggregates that measures the added value produced in a country during a period. In the contemporary world, macroeconomic uncertainty, among others due to the COVID-19 pandemic and the conflict in Ukraine, and GDP prediction remain important goals in public policy making. This study aims to predict Benin's GDP through a unidimensional statistical approach and machine learning techniques. For this purpose, GDP data were collected from the Central Bank of the West African States (BCEAO) website from 1960 to 2021. The predictions are based on comparing classical statistical and machine learning methods. For the classical statistical methods, we investigated the Autoregressive Integrated Moving Average (ARIMA) and Error Trend Seasonality (ETS) forecasting models. As for the machine learning methods, the K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM) forecasting models proved to be sound. The findings revealed that the statistical models (ARIMA and ETS) better predict Benin's GDP. However, machine learning models (KNN and LSTM) also provide a wide range of results that can be used to analyze Benin's economic growth.