Nowcasting Philippine Household Consumption: An Alternative Approach using Google Trends and XGBoost Model

Abstract

[EN] This study aims to develop a model for nowcasting household consumption in the Philippines using alternative data and machine learning model. In particular, we utilize Google search queries and Extreme Gradient Boosting (XGBoost) to nowcast household consumption. Our results indicate that XGBoost model outperforms benchmark autoregressive models. Shapley Additive explanations suggest that the top features of the XGBoost model are lags of household consumption and Google search indices related to travel. Overall, we demonstrate the potential use of Google Trends in capturing the likely trends in household spending in the near term.