Todd E. Clark, Florian Huber, Gary Koop, Massimiliano Marcellino
arXiv 28 Feb 2022 · Econometrics · publishedThe Annals of Applied Statistics (2024) · 15 citations (OpenAlex)
arXiv:2202.13793 · PDF · DOI · OpenAlex · Extracted main text
The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks. Inspired by these concerns, we develop a model for inflation forecasting that is nonparametric both in the conditional mean and in the error using Gaussian and Dirichlet processes, respectively. We discuss how both these features may be important in producing accurate forecasts of inflation. In a forecasting exercise involving CPI inflation, we find that our approach has substantial benefits, both overall and in the left tail, with nonparametric modeling of the conditional mean being of particular importance.
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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 | Shin, Bhattacharya, and Johnson (2020) Functional horseshoe priors for subspace shrinkage | 0.956 | 8 | 3 | 88% |
| 2 | Stock and Watson (2007) Why has U.S. inflation become harder to forecast? | 0.811 | 4 | 2 | 100% |
| 3 | Stock and Watson (2010) Modeling inflation after the Crisis | 0.644 | 2 | 2 | 100% |
| 4 | Rossi and Sekhposyan (2019) Alternative tests for correct specification of conditional predictive densities | 0.585 | 3 | 1 | 100% |
| 5 | Frühwirth-Schnatter and Malsiner-Walli (2019) From here to infinity: sparse finite versus dirichlet process mixtures in model based clustering | 0.511 | 3 | 2 | 33% |
| 6 | McCracken and Ng (2020) FRED-QD: A quarterly database for macroeconomic research | 0.511 | 2 | 2 | 50% |
| 7 | Escobar and West (1995) Bayesian density estimation and inference using mixtures | 0.511 | 2 | 1 | 100% |
| 8 | Faust and Wright (2013) Forecasting inflation | 0.511 | 2 | 1 | 100% |
| 9 | Woody, Carvalho, and Murray (2021) Model interpretation through lower-dimensional posterior summarization | 0.511 | 2 | 1 | 100% |
| 10 | Babb and Detmeister (2017) Nonlinearities in the Phillips curve for the United States: Evidence using metropolitan data | 0.405 | 1 | 1 | 100% |
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