Niko Hauzenberger, Massimiliano Marcellino, Michael Pfarrhofer, Anna Stelzer
arXiv 16 Feb 2024 · Econometrics · 4 citations (OpenAlex)
arXiv:2402.10574 · PDF · DOI · OpenAlex · Extracted main text
We develop Bayesian machine learning methods for mixed data sampling (MIDAS) regressions. This involves handling frequency mismatches and specifying functional relationships between many predictors and the dependent variable. We use Gaussian processes (GPs) and compress the input space with structured and unstructured MIDAS variants. This yields several versions of GP-MIDAS with distinct properties and implications, which we evaluate in short-horizon now- and forecasting exercises with both simulated data and data on quarterly US output growth and inflation in the GDP deflator. It turns out that our proposed framework leverages macroeconomic Big Data in a computationally efficient way and offers gains in predictive accuracy compared to other machine learning approaches along several dimensions.
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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 | Mogliani M, and Simoni A (2021) Bayesian MIDAS penalized regressions: estimation, selection, and prediction | 1.000 | 6 | 4 | 100% |
| 2 | Babii A, Ghysels E, and Striaukas J (2022) Machine learning time series regressions with an application to nowcasting | 0.956 | 8 | 5 | 88% |
| 3 | Mogliani M, and Simoni A (2024) Bayesian Bi-level Sparse Group Regressions for Macroeconomic Forecasting | 0.941 | 6 | 4 | 83% |
| 4 | Clark TE, Huber F, Koop G, Marcellino M, and Pfarrhofer M (2023) Tail forecasting with multivariate bayesian additive regression trees | 0.928 | 4 | 3 | 100% |
| 5 | Ghysels E, Sinko A, and Valkanov R (2007) MIDAS regressions: Further results and new directions | 0.928 | 4 | 3 | 100% |
| 6 | Chipman HA, George EI, and McCulloch RE (2010) BART: Bayesian additive regression trees | 0.843 | 4 | 3 | 75% |
| 7 | Clark TE, Huber F, Koop G, and Marcellino M (2024) a), Forecasting US inflation using Bayesian nonparametric models | 0.843 | 3 | 3 | 100% |
| 8 | Chan JCC, Poon A, and Zhu D (2024) Time-Varying Parameter MIDAS Models: Application to Nowcasting US Real GDP | 0.737 | 3 | 3 | 67% |
| 9 | Guhaniyogi R, and Dunson DB (2016) Compressed Gaussian process for manifold regression | 0.737 | 3 | 2 | 100% |
| 10 | Andreou E, Ghysels E, and Kourtellos A (2010) Regression models with mixed sampling frequencies | 0.644 | 2 | 2 | 100% |
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