A1774
Title: Forecasting Philippine quarterly GDP using dynamic factor model with mixed-frequency data
Authors: Rutcher Lacaza - University of the Philippines (Philippines) [presenting]
Stephen Jun Villejo - University of Glasgow (United Kingdom)
Abstract: Considering the impact of the COVID-19 pandemic, forecasting GDP growth is crucial for the Philippine government as it strives to achieve annual economic growth targets of 6.5\% to 8\% from 2023 to 2028 under its medium-term fiscal framework (MTFF). Traditional forecasting models for economic growth rely on aggregating economic or financial indicators observed at higher frequencies than quarterly GDP growth, which can lead to a loss of useful forward-looking information and less accurate forecasts. To address this issue, a dynamic factor with mixed-frequency data is applied to forecast quarterly GDP growth in the Philippines based on the selected monthly indicators from 2000 to 2023. The results indicate that forecasts using dynamic predictors with mixed-frequency data have better accuracy compared to traditional forecasting methods. The findings demonstrate the usefulness of mixed-frequency models in providing timely and accurate information to policymakers, enabling informed decisions, especially in the post-pandemic period.
