Tracking the economy at high frequency

TL;DR

This paper introduces a Bayesian dynamic factor model with mixed-frequency data to create a real-time high-frequency economic indicator, effectively handling asynchronous and sparse official statistics for timely economic monitoring.

Contribution

It presents a novel framework that combines mixed-frequency data and a pseudo-week structure to produce a unified high-frequency economic index, applicable in data-scarce environments.

Findings

Successful construction of a high-frequency economic index for Ecuador.

Effective real-time recession probability estimation using the index.

Robust handling of asynchronous and sparse official data.

Abstract

This paper develops a high-frequency economic indicator using a Bayesian Dynamic Factor Model estimated with mixed-frequency data. The model incorporates weekly, monthly, and quarterly official indicators, and allows for dynamic heterogeneity and stochastic volatility. To ensure temporal consistency and avoid irregular aggregation artifacts, we introduce a pseudo-week structure that harmonizes the timing of observations. Our framework integrates dispersed and asynchronous official statistics into a unified High-Frequency Economic Index (HFEI), enabling real-time economic monitoring even in environments characterized by severe data limitations. We apply this framework to construct a high-frequency indicator for Ecuador, a country where official data are sparse and highly asynchronous, and compute pseudo-weekly recession probabilities using a time-varying mean regime-switching model fitted to the resulting index.