Statistical methods for inflation forecasting in Morocco: Insights from Google trends data

Abstract

Accurate inflation forecasting is essential for effective economic planning and policy-making. The increasing use of the internet enables user generated content to capture people’s expectations and perspectives on economic issues. This study aims to investigate the power of Google trends data as an effective alternative source of data for forecasting inflation in Morocco. By identifying keywords that exhibit Granger causality with the inflation rate, we examined the linear effect of public interest on inflation forecasting using a principal component index as an exogenous factor to enhance outcomes. The selected SARIMA model, coupled with the resulting index, presents an optimal trajectory for inflation rate. The results of this study demonstrate that the model incorporating Google Trends data yielded the best performance based on evaluation measures such as AIC, RMSE, and log-likelihood. This highlights that the Google index is a significant factor for accurately explaining and forecasting inflation rate movements, contributing substantially to inflation modeling. The adaptive features of our approach make it preferably suited to describing inflation uncertainty when the economy is subject to constantly changing monetary institutions and policies.