arXiv 19 Mar 2019 · Econometrics · publishedJournal of Econometrics (2020) · 64 citations (OpenAlex)
arXiv:1903.08025 · PDF · DOI · OpenAlex · Extracted main text
We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian techniques for estimation and inference. In particular, to improve the prediction properties of the model and its sparse recovery ability, we consider a Group Lasso with a spike-and-slab prior. Penalty hyper-parameters governing the model shrinkage are automatically tuned via an adaptive MCMC algorithm. We establish good frequentist asymptotic properties of the posterior of the in-sample and out-of-sample prediction error, we recover the optimal posterior contraction rate, and we show optimality of the posterior predictive density. Simulations show that the proposed models have good selection and forecasting performance in small samples, even when the design matrix presents cross-correlation. When applied to forecasting U.S. GDP, our penalized regressions can outperform many strong competitors. Results suggest that financial variables may have some, although very limited, short-term predictive content.
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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 | Uematsu, Y., Tanaka, S (2019) High-dimensional macroeconomic forecasting and variable selection via penalized regression | 1.000 | 7 | 4 | 100% |
| 2 | Pettenuzzo, D., Timmermann, A., Valkanov, R (2016) A MIDAS approach to modeling first and second moment dynamics | 1.000 | 5 | 3 | 100% |
| 3 | Kyung, M., Gill, J., Ghosh, M., Casella, G (2010) Penalized regression, standard errors, and Bayesian lassos | 0.928 | 4 | 3 | 100% |
| 4 | Ning, B., Jeong, S., Ghosal, S (2020) Bayesian linear regression for multivariate responses under group sparsity | 0.909 | 8 | 4 | 75% |
| 5 | Lounici, K., Pontil, M., van de Geer, S., Tsybakov, A. B (2011) Oracle inequalities and optimal inference under group sparsity | 0.874 | 5 | 2 | 100% |
| 6 | Atchadé, Y. F (2011) A computational framework for empirical Bayes inference | 0.843 | 4 | 3 | 75% |
| 7 | Andreou, E., Ghysels, E., Kourtellos, A (2010) Regression models with mixed sampling frequencies | 0.843 | 3 | 3 | 100% |
| 8 | Xu, X., Ghosh, M (2015) Bayesian variable selection and estimation for group lasso | 0.843 | 3 | 3 | 100% |
| 9 | Yuan, M., Lin, Y (2006) Model selection and estimation in regression with grouped variables | 0.843 | 3 | 3 | 100% |
| 10 | Andreou, E., Ghysels, E., Kourtellos, A (2013) Should macroeconomic forecasters use daily financial data and how? Journal of Business & Economic Statistics 31 (2), 240–251 | 0.811 | 4 | 2 | 100% |
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