arXiv 8 Feb 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 10 citations (OpenAlex)
arXiv:2202.04146 · PDF · DOI · OpenAlex · Extracted main text
Many problems plague empirical Phillips curves (PCs). Among them is the hurdle that the two key components, inflation expectations and the output gap, are both unobserved. Traditional remedies include proxying for the absentees or extracting them via assumptions-heavy filtering procedures. I propose an alternative route: a Hemisphere Neural Network (HNN) whose architecture yields a final layer where components can be interpreted as latent states within a Neural PC. There are benefits. First, HNN conducts the supervised estimation of nonlinearities that arise when translating a high-dimensional set of observed regressors into latent states. Second, forecasts are economically interpretable. Among other findings, the contribution of real activity to inflation appears understated in traditional PCs. In contrast, HNN captures the 2021 upswing in inflation and attributes it to a large positive output gap starting from late 2020. The unique path of HNN's gap comes from dispensing with unemployment and GDP in favor of an amalgam of nonlinearly processed alternative tightness indicators.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Goulet Coulombe, P (2024) The macroeconomy as a random forest | 0.961 | 9 | 5 | 89% |
| 2 | Coibion, O. and Gorodnichenko, Y (2015) Is the phillips curve alive and well after all? inflation expectations and the missing disinflation | 0.941 | 6 | 4 | 83% |
| 3 | Chan, J. C., Koop, G., and Potter, S. M (2016) A bounded model of time variation in trend inflation, nairu and the phillips curve | 0.928 | 4 | 3 | 100% |
| 4 | Hasenzagl, T., Pellegrino, F., Reichlin, L., and Ricco, G (2018) A model of the fed's view on inflation | 0.928 | 4 | 3 | 100% |
| 5 | Blanchard, O., Cerutti, E., and Summers, L (2015) Inflation and activity–two explorations and their monetary policy implications | 0.874 | 6 | 4 | 67% |
| 6 | McCracken, M. and Ng, S (2020) Fred-qd: A quarterly database for macroeconomic research | 0.874 | 6 | 3 | 67% |
| 7 | Belkin, M., Hsu, D., Ma, S., and Mandal, S (2019) Reconciling modern machine-learning practice and the classical bias–variance trade-off | 0.843 | 4 | 3 | 75% |
| 8 | Del Negro, M., Lenza, M., Primiceri, G. E., and Tambalotti, A (2020) What’s up with the phillips curve? | 0.843 | 4 | 3 | 75% |
| 9 | Stock, J. H. and Watson, M. W (2008) Phillips curve inflation forecasts | 0.843 | 5 | 3 | 60% |
| 10 | Stock, J. H. and Watson, M. W (2019) Slack and cyclically sensitive inflation | 0.737 | 4 | 3 | 50% |
Showing the top 10 of 75 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 0.5cm dpd LGB+: A Macroeconomic Forecasting Road Test . 0.25cm | 0.644 | 3 | 2 |
| 2 | Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty | 0.405 | 1 | 1 |
| 3 | Nonlinear Dynamic Factor Analysis With a Transformer Network | 0.405 | 1 | 1 |