Dynamic Principal Component Analysis for the Construction of High-Frequency Economic Indicators
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
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.
Recommended Citation
Lim, B.G., Ong, H.J., Tan, R.R., Ikeda, K. (2024). Dynamic Principal Component Analysis for the Construction of High-Frequency Economic Indicators. In: Thiruchelvam, V., Alfred, R., Ismail, Z.I.B.A., Haviluddin, H., Baharum, A. (eds) Proceedings of the 4th International Conference on Advances in Computational Science and Engineering. ICACSE 2023. Lecture Notes in Electrical Engineering, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-97-2977-7_40
