Bruno P. C. Levy, Hedibert F. Lopes
arXiv 11 Jan 2021 · Econometrics · 3 citations (OpenAlex)
arXiv:2101.04164 · PDF · DOI · OpenAlex · Extracted main text
In many fields where the main goal is to produce sequential forecasts for decision making problems, the good understanding of the contemporaneous relations among different series is crucial for the estimation of the covariance matrix. In recent years, the modified Cholesky decomposition appeared as a popular approach to covariance matrix estimation. However, its main drawback relies on the imposition of the series ordering structure. In this work, we propose a highly flexible and fast method to deal with the problem of ordering uncertainty in a dynamic fashion with the use of Dynamic Order Probabilities. We apply the proposed method in two different forecasting contexts. The first is a dynamic portfolio allocation problem, where the investor is able to learn the contemporaneous relationships among different currencies improving final decisions and economic performance. The second is a macroeconomic application, where the econometrician can adapt sequentially to new economic environments, switching the contemporaneous relations among macroeconomic variables over time.
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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 | Primiceri, G. E (2005) Time varying structural vector autoregressions and monetary policy | 1.000 | 12 | 3 | 100% |
| 2 | Beckmann, J., G. Koop, D. Korobilis, and R. A. Schüssler (2020) Exchange rate predictability and dynamic Bayesian learning | 1.000 | 10 | 3 | 100% |
| 3 | Zhao, Z. Y., M. Xie, and M. West (2016) Dynamic dependence networks: Financial time series forecasting and portfolio decisions | 0.969 | 11 | 6 | 91% |
| 4 | Raftery, A. E., M. Kárnỳ, and P. Ettler (2010) Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill | 0.961 | 9 | 4 | 89% |
| 5 | Koop, G. and D. Korobilis (2013) Large time-varying parameter VARs | 0.956 | 8 | 6 | 88% |
| 6 | Fisher, J. D., D. Pettenuzzo, C. M. Carvalho, et al (2020) Optimal asset allocation with multivariate Bayesian dynamic linear models | 0.843 | 4 | 3 | 75% |
| 7 | Levy, B. P. and H. F. Lopes (2021) Trend-Following Strategies via Dynamic Momentum Learning self | 0.843 | 3 | 3 | 100% |
| 8 | Lavine, I., M. Lindon, M. West, et al (2020) Adaptive variable selection for sequential prediction in multivariate dynamic models | 0.811 | 4 | 2 | 100% |
| 9 | Lopes, H. F., R. E. McCulloch, and R. S. Tsay (2018) Parsimony inducing priors for large scale state-space models self | 0.811 | 4 | 2 | 100% |
| 10 | West, M. and J. Harrison (1997) Bayesian forecasting and dynamic models | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 46 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Dynamic Portfolio Allocation in High Dimensions using Sparse Risk Factors | 0.965 | 10 | 4 |