Re-examining the Validity of the Augmented Phillips Curve in the United States of America
Bibliographic record
Abstract
Aims: This study empirically tests the validity of the augmented Phillips curve hypothesis for the United States using quarterly data from 1982Q1 to 2023Q4. The augmented Phillips curve posits an inverse relationship between the unemployment gap and inflation, with inflationary expectations playing a crucial mediating role. Study Design: Time series econometric analysis with structural break testing. Place and Duration of Study: United States economy, from the first quarter of 1982 to the fourth quarter of 2023 (168 quarterly observations). Methodology: The analysis employs data from the Federal Reserve Economic Data (FRED) database. Inflation is measured by the year-over-year percentage change in the PCEPI. Independent variables include the unemployment gap (actual unemployment rate minus natural rate) and expected inflation from the University of Michigan Survey. April 2009 is selected as a potential structural break point because it corresponds to the trough of the Global Financial Crisis, a period widely documented as having altered U.S. inflation dynamics. Initial estimation uses Ordinary Least Squares (OLS), but diagnostic testing reveals significant autocorrelation. Therefore, the study transitions to Generalized Least Squares (GLS) to obtain more efficient and reliable estimates. Results: The OLS model produces a theoretically divergent positive unemployment-gap coefficient, reflecting misspecification driven by autocorrelation and crisis-period instability. However, after correcting for autocorrelation through GLS, the unemployment-gap coefficient attains the theoretically consistent negative sign predicted by the augmented Phillips curve. The GLS results confirm a statistically significant negative relationship between the unemployment gap and inflation (β₂ = –0.14, p = .001) and a positive relationship between expected inflation and actual inflation (β₃ = 0.63, p < .001). Structural break analysis further indicates that inflation averaged 0.94 percentage points lower in the post-crisis period (δ₁ = –0.94, p = .03), with the Chow test validating the April 2009 break (F = 5.03, p = .02). Conclusion: The findings support the augmented Phillips curve once autocorrelation and structural instability are addressed. The results highlight the importance of accounting for crisis-induced regime shifts, properly modeling inflation expectations, and correcting serial correlation when estimating inflation dynamics in advanced economies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".