An Adaptive Moving Average for Macroeconomic Monitoring

TL;DR

This paper introduces an adaptive moving average method using Random Forests to improve macroeconomic series monitoring, effectively balancing responsiveness and noise reduction amid changing economic conditions.

Contribution

The paper proposes a novel adaptive moving average estimator based on Random Forests that dynamically adjusts to macroeconomic conditions, enhancing signal detection in economic indicators.

Findings

Different narratives on post-pandemic inflation dynamics compared to traditional methods.

The adaptive estimator captures rapid economic changes more effectively.

It provides a more nuanced view of inflation acceleration and deceleration.

Abstract

The use of moving averages is pervasive in macroeconomic monitoring, particularly for tracking noisy series such as inflation. The choice of the look-back window is crucial. Too long of a moving average is not timely enough when faced with rapidly evolving economic conditions. Too narrow averages are noisy, limiting signal extraction capabilities. As is well known, this is a bias-variance trade-off. However, it is a time-varying one: the optimal size of the look-back window depends on current macroeconomic conditions. In this paper, we introduce a simple adaptive moving average estimator based on a Random Forest using as sole predictor a time trend. Then, we compare the narratives inferred from the new estimator to those derived from common alternatives across series such as headline inflation, core inflation, and real activity indicators. Notably, we find that this simple tool provides a different account of the post-pandemic inflation acceleration and subsequent deceleration.