Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn, Ruben Loaiza-Maya, Florian Huber, Gary Koop, John Maheu, Didier Nibbering, Anastasios Panagiotelis
arXiv 7 Dec 2022 · Econometrics · publishedInternational Journal of Forecasting (2023) · 38 citations (OpenAlex)
arXiv:2212.03471 · PDF · DOI · OpenAlex · Extracted main text
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified explicitly, and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context; and with sufficient computational detail given to assist the reader with implementation.
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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 | Geweke, J. and Whiteman, C (2006) Bayesian forecasting | 0.928 | 4 | 3 | 100% |
| 2 | Gneiting, T. and Raftery, A. E (2007) Strictly proper scoring rules, prediction, and estimation | 0.874 | 5 | 2 | 100% |
| 3 | Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H.,… (1953) Equations of state calculations by fast computing machines | 0.811 | 4 | 2 | 100% |
| 4 | Rue, H., Martino, S., and Chopin, N (2009) Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations | 0.737 | 5 | 3 | 40% |
| 5 | Andrieu, C., Doucet, A., and Holenstein, R (2011) Particle Markov chain Monte Carlo | 0.737 | 3 | 3 | 67% |
| 6 | Hastings, W (1970) Monte Carlo sampling methods using Markov chains and their application | 0.737 | 3 | 2 | 100% |
| 7 | Martin, G. M., McCabe, B. P., Frazier, D. T., Maneesoonthorn, W., an… (2019) Auxiliary likelihood-based approximate Bayesian computation in state space models self | 0.737 | 3 | 2 | 100% |
| 8 | Loaiza-Maya, R., Martin, G. M., and Frazier, D. T (2021) Focused Bayesian prediction self | 0.693 | 5 | 1 | 100% |
| 9 | Huber, F., Koop, G., Onorante, L., Pfarrhofer, M., and Schreiner, J (2023) Nowcasting in a pandemic using non-parametric mixed frequency VARs self | 0.644 | 4 | 1 | 100% |
| 10 | Baştürk, N., Borowska, A., Grassi, S., Hoogerheide, L., and van Dijk… (2019) Forecast density combinations of dynamic models and data driven portfolio strategies | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 344 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | RoyalBlue4 Decision Synthesis in Monetary Policy | 0.405 | 1 | 1 |
| 2 | Utility-Weighted Forecasting and Calibration for Risk-Adjusted Decisions under Trading Frictions | 0.405 | 1 | 1 |