arXiv 24 Jun 2026 · Econometrics
arXiv:2606.25688 · PDF · DOI · OpenAlex · Extracted main text
Structural models often fix (calibrate) some parameters and estimate the rest, but this calibration-estimation partition is usually chosen by convention. This paper treats that choice as an econometric partition-selection problem. For each admissible partition, we construct a scalar sensitivity statistic measuring the local response of a target object -- such as a policy effect, welfare measure, impulse response, or treatment effect -- to perturbations of the calibrated parameters. The selected partition minimizes this statistic and therefore minimizes worst-case local bias from calibration errors. We first illustrate the decision problem in two canonical examples. We then apply it to the New Keynesian model of Nakamura and Steinsson (2018), where the partition choice has large implications for credibility: some partitions remain reliable under sizeable miscalibrations, whereas others generate large bias from small calibration errors. The procedure requires only local derivatives, avoids repeated re-estimation, and applies to a broad class of structural models.
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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 | Nakamura, Emi and Jón Steinsson (2018) High-frequency identification of monetary non-neutrality: the information effect | 1.000 | 10 | 5 | 100% |
| 2 | An, Sungbae and Frank Schorfheide (2007) Bayesian analysis of dsge models | 1.000 | 5 | 3 | 100% |
| 3 | Bonhomme, Stéphane and Martin Weidner (2022) Minimizing sensitivity to model misspecification | 0.874 | 6 | 2 | 100% |
| 4 | Kalouptsidi, Myrto, Paul T Scott, and Eduardo Souza-Rodrigues (2021) Identification of counterfactuals in dynamic discrete choice models | 0.874 | 5 | 2 | 100% |
| 5 | Jrgensen, Thomas H (2023) Sensitivity to calibrated parameters | 0.811 | 4 | 2 | 100% |
| 6 | Forneron, Jean-Jacques (2024) Detecting identification failure in moment condition models | 0.737 | 3 | 2 | 100% |
| 7 | Smets, Frank and Rafael Wouters (2007) Shocks and frictions in us business cycles: A bayesian dsge approach | 0.693 | 5 | 1 | 100% |
| 8 | Aguirregabiria, Victor and Junichi Suzuki (2014) Identification and counterfactuals in dynamic models of market entry and exit | 0.644 | 2 | 2 | 100% |
| 9 | Alegre, Joan and Juan Carlos Escanciano (2023) Robust minimum distance inference in structural models | 0.644 | 2 | 2 | 100% |
| 10 | Hazell, Jonathon, Juan Herreno, Emi Nakamura, and Jón Steinsson (2022) The slope of the phillips curve: evidence from us states | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 48 scored citations.