Dynamic Principal Component Analysis for the Construction of High-Frequency Economic Indicators

• Main Authors: Lim, Brian Godwin, Ong, Hans Jarett, Tan, Renzo Roel P, Ikeda, Kazushi
• Format: text
• Published: Archīum Ateneo 2024
• Subjects: Anomaly detection; Dynamic principal component analysis; Economic indicator; Computer Sciences; Databases and Information Systems; Physical Sciences and Mathematics
• Online Access: https://archium.ateneo.edu/qmit-faculty-pubs/31; https://doi.org/10.1007/978-981-97-2977-7_40
• Institution: Ateneo De Manila University

Recent progress in data analysis and machine learning has enabled the efficient processing of large data; however, the public sector has yet to fully adopt these advancements. The study investigates the application of dynamic principal component analysis in offering real-time insights into various facets of an economy, potentially aiding in the informed decision-making of policymakers. In brief, dynamic principal component analysis generates dynamic principal components representing latent factors that account for the autocovariance in time series data. In examining daily data from the Philippine stock exchange, Philippine peso exchange rates, and Philippine peso to United States dollar forward rates, results demonstrate the effectiveness of the first three dynamic principal components as high-frequency indicators for business and investment conditions, economic performance, and economic outlook, respectively. Moreover, an application of the isolation forest anomaly detection algorithm validates the sensitivity of the constructed indicators to systematic economic shocks, which identified events such as the taper tantrum of 2013 and the 2020 lockdown due to the novel coronavirus pandemic, among others. Overall, the practical applicability of the proposed methodology suggests potential extensions incorporating nontraditional data sources for more comprehensive economic indicators.