Paper: arXiv 2601.11601

Authors: Daniil Bargman, Francesca Medda, Akash Sedai Sharma

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.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides a strong empirical re-evaluation of the Phillips curve using a large horse race of 3,968 factor combinations and out-of-sample backtesting against univariate benchmarks. While relying on Latent Shock Regression (LSR) and standard econometric methods rather than heavy mathematical derivations, its conceptual reformulation of price pressure as a latent process offers high practical value and clarity.

Research Flowchart

  flowchart TD
    A[Research Goal: Evaluate Latent Variable PC<br/>for Medium-Term Inflation Forecasting] --> B[Data Inputs: US Core PCE Inflation<br/>Q1 1983 – Q1 2025]
    B --> C[Methodology: Generate 3,968 Random<br/>Factor Combinations & Test MA1 Residuals]
    C --> D[Evaluate Forecast Accuracy:<br/>Compare Latent PC, Traditional PC, & Univariate Benchmark]
    D --> E1[Outcomes: Latent PC outperforms Traditional PC<br/>6-8 quarters ahead]
    D --> E2[Outcomes: MA1 process improves overall accuracy;<br/>Corroborates new PC theoretical view]

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0 of 58 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.