Alisa Yusupova, Nicos G. Pavlidis, Efthymios G. Pavlidis
arXiv 10 Dec 2019 · Econometrics · 4 citations (OpenAlex)
arXiv:1912.04661 · PDF · DOI · OpenAlex · Extracted main text
Dynamic model averaging (DMA) combines the forecasts of a large number of dynamic linear models (DLMs) to predict the future value of a time series. The performance of DMA critically depends on the appropriate choice of two forgetting factors. The first of these controls the speed of adaptation of the coefficient vector of each DLM, while the second enables time variation in the model averaging stage. In this paper we develop a novel, adaptive dynamic model averaging (ADMA) methodology. The proposed methodology employs a stochastic optimisation algorithm that sequentially updates the forgetting factor of each DLM, and uses a state-of-the-art non-parametric model combination algorithm from the prediction with expert advice literature, which offers finite-time performance guarantees. An empirical application to quarterly UK house price data suggests that ADMA produces more accurate forecasts than the benchmark autoregressive model, as well as competing DMA specifications.
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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 | Koop, G. and D. Korobilis (2012) Forecasting inflation using dynamic model averaging | 1.000 | 11 | 3 | 100% |
| 2 | Raftery, A. E., M. Kárný, and P. Ettler (2010) Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill | 1.000 | 9 | 3 | 100% |
| 3 | V'yugin, V. and V. Trunov (2019) Online aggregation of unbounded losses using shifting experts with confidence | 1.000 | 8 | 3 | 100% |
| 4 | Dangl, T. and M. Halling (2012) Predictive regressions with time-varying coefficients | 1.000 | 7 | 3 | 100% |
| 5 | Catania, L. and N. Nonejad (2018) Dynamic model averaging for practitioners in economics and finance: The eDMA package | 0.950 | 7 | 4 | 86% |
| 6 | Bork, L. and S. V. Mller (2015) Forecasting house prices in the 50 states using dynamic model averaging and dynamic model selection | 0.811 | 4 | 2 | 100% |
| 7 | Chen, B. and Y. Hong (2012) Testing for smooth structural changes in time series models via nonparametric regression | 0.811 | 4 | 2 | 100% |
| 8 | Byrne, J. P., D. Korobilis, and P. J. Ribeiro (2018) On the source of uncertainty in exchange rate predictability | 0.737 | 3 | 2 | 100% |
| Pavlidis | unmatched citation key Pavlidis | 0.644 | 5 | 2 | 40% |
| Adams | unmatched citation key Adams | 0.644 | 4 | 2 | 50% |
Showing the top 10 of 124 scored citations. 2 of these could not be matched to a bibliography entry, so only the citation key is shown.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Forecasting: theory and practice | 0.511 | 2 | 1 |