arXiv 3 Apr 2024 · Econometrics
arXiv:2404.02671 · PDF · DOI · OpenAlex · Extracted main text
We propose a Machine Learning approach for optimal macroeconomic density forecasting in a high-dimensional setting where the underlying model exhibits a known group structure. Our approach is general enough to encompass specific forecasting models featuring either many covariates, or unknown nonlinearities, or series sampled at different frequencies. By relying on the novel concept of bi-level sparsity in time-series econometrics, we construct density forecasts based on a prior that induces sparsity both at the group level and within groups. We demonstrate the consistency of both posterior and predictive distributions. We show that the posterior distribution contracts at the minimax-optimal rate and, asymptotically, puts mass on a set that includes the support of the model. Our theory allows for correlation between groups, while predictors in the same group can be characterized by strong covariation as well as common characteristics and patterns. Finite sample performance is illustrated through comprehensive Monte Carlo experiments and a real-data nowcasting exercise of the US GDP growth rate.
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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., Simoni, A (2021) Bayesian MIDAS penalized regressions: Estimation, selection, and prediction self | 1.000 | 7 | 4 | 100% |
| 2 | Xu, X., Ghosh, M (2015) Bayesian variable selection and estimation for Group Lasso | 0.843 | 3 | 3 | 100% |
| 3 | Carriero, A., Clark, T.E., Marcellino, M., Mertens, E (2024) Addressing COVID-19 outliers in BVARs with stochastic volatility | 0.737 | 3 | 2 | 100% |
| 4 | Li, Z., Zhang, Y., Yin, J (2024) Estimating double sparse structures over $_u(_q)$-balls: Minimax rates and phase transition | 0.737 | 3 | 2 | 100% |
| 5 | McCracken, M.W., Ng, S (2016) FRED-MD: A monthly database for macroeconomic research | 0.737 | 3 | 2 | 100% |
| 6 | Cai, T.T., Zhang, A.R., Zhou, Y (2022) Sparse Group Lasso: Optimal sample complexity, convergence rate, and statistical inference | 0.644 | 2 | 2 | 100% |
| 7 | Chan, J.C.C., Hsiao, C (2014) Estimation of stochastic volatility models with heavy tails and serial dependence | 0.644 | 2 | 2 | 100% |
| 8 | Foroni, C., Marcellino, M., Schumacher, C (2015) Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials | 0.644 | 2 | 2 | 100% |
| 9 | Zhang, B., Chan, J.C.C., Cross, J.L (2020) Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts | 0.644 | 2 | 2 | 100% |
| 10 | Hoffmann, M., Rousseau, J., Schmidt-Hieber, J (2015) On adaptive posterior concentration rates | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 34 scored citations.