🤖 AI Summary
This study addresses the ad hoc division between calibrated and estimated parameters in structural modeling, which often lacks a systematic foundation and can induce substantial bias due to calibration errors. For the first time, the partitioning problem is formalized as an optimization task, and a sensitivity-minimization criterion is proposed for selecting the optimal split. Specifically, a sensitivity statistic—constructed from local derivatives—quantifies how target estimates respond to perturbations in calibrated parameters. The method selects the partition that minimizes this statistic, thereby reducing worst-case local bias. Notably, it avoids repeated re-estimation and is applicable across a broad class of structural models. An application to a New Keynesian model demonstrates that the chosen partition significantly enhances estimation robustness and credibility under sizable calibration errors.
📝 Abstract
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.