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Aggregating many estimators using estimated weights

Emmanuel Guerre, Yuting Wang

arXiv 16 Sep 2026 · Econometrics

arXiv:2609.19415 · PDF · Extracted main text

Abstract

Consider an increasing number of consistent estimators to be averaged when only estimated weights are available. The underlying parameter of interest can be identical across estimators (homogeneity) or not (heterogeneity). The contribution of the paper is threefold. First, it is shown that the interaction of the estimated weights with the estimators can generate specific bias terms. This constrains the number of estimators that can be aggregated when weight estimation is ignored in inference. Second, the paper proposes estimated adaptive weights, which allow for standard Gaussian inference in a uniform manner and are asymptotically optimal both under homogeneity and heterogeneity. Third, conditions ensuring the validity of the Cochran (1937) Q test of homogeneity with estimated variance are given.

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53
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101
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Fernandez-Val, Ivan and Gao, Wayne Yuan and Liao, Yuan and Vella, Fr… (2026) Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes1.00094100%
2Cochran, William G (1937) Problems Arising in the Analysis of Similar Experiments1.00086100%
3Gu, J. and Chen, Song Xi (2023) Distributed Statistical Inference under Heterogeneity1.00074100%
4Lu, Xun and Su, Liangjun (2023) Uniform Inference in Linear Panel Data Models with Two-Dimensional Heterogeneity1.00063100%
5Pesaran, M. Hashem and Yamagata, Takashi (2008) Testing Slope Homogeneity in Large Panels0.92844100%
6Ritz, John and Demidenko, Eugene and Spiegelman, Donna (2008) Multivariate Meta-Analysis for Data Consortia, Individual Patient Meta-Analysis, and Pooling Projects0.92844100%
7Swamy, P. A. V. B (1970) Efficient Inference in a Random Regression Model0.92843100%
8Vivalt, Eva (2020) How Much Can We Generalize from Impact Evaluations?0.92843100%
9Hedges, Larry V. and Olkin, Ingram (1985) Statistical Methods for Meta-Analysis0.84333100%
10Zeng, Donglin and Lin, D. Y (2015) On Random-Effects Meta-Analysis0.81142100%

Showing the top 10 of 53 scored citations.