Latent Variable Phillips Curve
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915736688001024 |
|---|---|
| author | Bargman, Daniil Medda, Francesca Sharma, Akash Sedai |
| author_facet | Bargman, Daniil Medda, Francesca Sharma, Akash Sedai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11601 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Latent Variable Phillips Curve Bargman, Daniil Medda, Francesca Sharma, Akash Sedai Statistical Finance General Economics Economics 91B64, 91B82, 91B84 (primary), 62P20 (secondary) 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. |
| title | Latent Variable Phillips Curve |
| topic | Statistical Finance General Economics Economics 91B64, 91B82, 91B84 (primary), 62P20 (secondary) |
| url | https://arxiv.org/abs/2601.11601 |