Modelling high frequency non-financial big time series with an application to jobless claims in Chile

This paper explores the challenges of modelling high-frequency, non-financial big data time-series. Focusing on daily, hourly, and even minute-level data, the study in-vestigates the presence of various seasonalities (daily, weekly, monthly, and annual) and how these cycles might interrelate between them and be influenced by weather patterns and calendar variations. By analyzing these cyclical characteristics and data responses to external factors, the paper explores the potential for regimeswitching, dynamic, and non-linear models to capture these complexities. Furthermore, it proposes the use of Autometrics –an automated algorithm for identifying parsimonious models– to jointly account for all the data’s peculiarities. The resulting models, beyond structural anal-ysis and forecasting, are useful for constructing real-time quantitative macroeconomic leading indicators, demand planning and dynamic pricing strategies in various sectors that are sensitive to the factors identified in the analysis (e.g., of utilities, retail stores, traffic, or labor market indicators). The paper includes an application to the daily series of jobless claims in Chile.