Zhongjun Qu, Wendun Wang, Xiaomeng Zhang
arXiv 17 Oct 2025 · Econometrics
arXiv:2510.16224 · PDF · DOI · OpenAlex · Extracted main text
A rich set of frequentist model averaging methods has been developed, but their applications have largely been limited to point prediction, as measuring prediction uncertainty in general settings remains an open problem. In this paper we propose prediction intervals for model averaging based on conformal inference. These intervals cover out-of-sample realizations of the outcome variable with a pre-specified probability, providing a way to assess predictive uncertainty beyond point prediction. The framework allows general model misspecification and applies to averaging across multiple models that can be nested, disjoint, overlapping, or any combination thereof, with weights that may depend on the estimation sample. We establish coverage guarantees under two sets of assumptions: exact finite-sample validity under exchangeability, relevant for cross-sectional data, and asymptotic validity under stationarity, relevant for time-series data. We first present a benchmark algorithm and then introduce a locally adaptive refinement and split-sample procedures that broaden applicability. The methods are illustrated with a cross-sectional application to real estate appraisal and a time-series application to equity premium forecasting.
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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 | Lei, J., A. R. Max G’Sell, R. J. Tibshirani, and L. Wasserman (2018) Distribution-free predictive inference for regression | 1.000 | 14 | 5 | 100% |
| 2 | Granger, C. W. J. and R. Ramanathan (1984) Improved methods of combining forecasts | 1.000 | 11 | 3 | 100% |
| 3 | Hansen, B. E (2007) Least squares model averaging | 1.000 | 10 | 3 | 100% |
| 4 | Hansen, B. E. and J. Racine (2012) Jacknife model averaging | 0.811 | 4 | 2 | 100% |
| 5 | Vovk, V., A. Gammerman, and G. Shafer (2005) Algorithmic Learning in a Random World | 0.811 | 4 | 2 | 100% |
| 6 | Buckland, S. T., K. P. Burnham, and N. H. Augustin (1997) Model selection: An integral part of inference | 0.737 | 3 | 2 | 100% |
| 7 | Goyal, A., I. Welch, and A. Zafirov (2024) A comprehensive 2022 look at the empirical performance of equity premium prediction | 0.737 | 3 | 2 | 100% |
| 8 | Tibshirani, R. J., R. Foygel Barber, E. Candes, and A. Ramdas (2019) Conformal prediction under covariate shift | 0.644 | 2 | 2 | 100% |
| 9 | Boudoukh, J., M. Richardson, and R. F. Whitelaw (2008) The myth of long-horizon predictability | 0.585 | 3 | 1 | 100% |
| 10 | Andrews, D. W. K (2003) End-of-sample instability tests | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 36 scored citations.