Raffaella Giacomini, Sokbae Lee, Silvia Sarpietro
arXiv 3 Aug 2023 · Econometrics
arXiv:2308.01596 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a novel approach to random effects estimation and individual-level forecasting in micropanels, targeting individual accuracy rather than aggregate performance. The conventional shrinkage methods used in the literature, such as the James-Stein estimator and Empirical Bayes, target aggregate performance and can lead to inaccurate decisions at the individual level. We propose a class of shrinkage estimators with individual weights (IW) that leverage an individual's own past history, instead of the cross-sectional dimension. This approach overcomes the "tyranny of the majority" inherent in existing methods, while relying on weaker assumptions. A key contribution is addressing the challenge of obtaining feasible weights from short time-series data and under parameter heterogeneity. We discuss the theoretical optimality of IW and recommend using feasible weights determined through a Minimax Regret analysis in practice.
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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 | Charles F Manski (2021) Econometrics for decision making: Building foundations sketched by Haavelmo and Wald | 1.000 | 6 | 3 | 100% |
| 2 | Bradley Efron and Carl Morris (1973) Stein's estimation rule and its competitors–-an empirical Bayes approach | 0.928 | 4 | 3 | 100% |
| 3 | Laura Liu, Hyungsik Roger Moon, and Frank Schorfheide (2020) Forecasting with dynamic panel data models | 0.874 | 10 | 2 | 100% |
| 4 | Patrick Kline, Evan K Rose, and Christopher R Walters (2022) Systemic discrimination among large US employers | 0.874 | 10 | 2 | 100% |
| 5 | Bradley Efron (2010) Large-scale inference, volume 1 of institute of mathematical statistics (IMS) monographs, 2010 | 0.874 | 5 | 2 | 100% |
| 6 | Jiaying Gu and Roger Koenker (2015) Unobserved heterogeneity in income dynamics: An empirical Bayes perspective | 0.874 | 5 | 2 | 100% |
| 7 | James H Stock and Mark W Watson (1998) A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series | 0.811 | 4 | 2 | 100% |
| 8 | W. James and Charles Stein (1961) Estimation with quadratic loss. proc. 4th Berkeley sympos. math. statist. and prob., vol. i. Berkeley, calif.: Univ. California… | 0.737 | 3 | 2 | 100% |
| 9 | Bradley Efron and Carl Morris (1971) Limiting the risk of Bayes and empirical Bayes estimators–-part i: the Bayes case | 0.737 | 3 | 2 | 100% |
| 10 | Bradley Efron (2016) Empirical Bayes deconvolution estimates | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Empirical Bayes Estimation in Heterogeneous Coefficient Panel Models | 0.405 | 1 | 1 |