Latent Variable Phillips Curve

TL;DR

This paper introduces a latent variable approach to the Phillips curve, demonstrating improved medium-term inflation forecasting accuracy over traditional models and suggesting new theoretical insights and practical improvements.

Contribution

It formulates and tests a latent variable Phillips curve model, showing its superior performance in medium-term inflation forecasting compared to traditional models.

Findings

Latent variable PC models outperform traditional models 6-8 quarters ahead.

Incorporating MA(1) residuals enhances model accuracy.

Findings support a new conceptual view of the Phillips curve.

Abstract

This paper re-examines the empirical Phillips curve (PC) model and its usefulness in the context of medium-term inflation forecasting. A latent variable Phillips curve hypothesis is formulated and tested using 3,968 randomly generated factor combinations. Evidence from US core PCE inflation between Q1 1983 and Q1 2025 suggests that latent variable PC models reliably outperform traditional PC models six to eight quarters ahead and stand a greater chance of outperforming a univariate benchmark. Incorporating an MA(1) residual process improves the accuracy of empirical PC models across the board, although the gains relative to univariate models remain small. The findings presented in this paper have two important implications: First, they corroborate a new conceptual view on the Phillips curve theory; second, they offer a novel path towards improving the competitiveness of Phillips curve forecasts in future empirical work.