Adapting to misspecification

Timothy B. Armstrong, Patrick Kline and Liyang Sun

No 18/24, CeMMAP working papers from Institute for Fiscal Studies

Abstract: Empirical research typically involves a robustness-efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more robust, but variable, unrestricted estimator. When a bound on the bias of the restricted estimator is available, it is optimal to shrink the unrestricted estimator towards the restricted estimator. For settings where a bound on the bias of the restricted estimator is unknown, we propose adaptive estimators that minimize the percentage increase in worst case risk relative to an oracle that knows the bound. We show that adaptive estimators solve a weighted convex minimax problem and provide lookup tables facilitating their rapid computation. Revisiting some well known empirical studies where questions of model specification arise, we examine the advantages of adapting to—rather than testing for—misspecification.

Date: 2024-10-01
New Economics Papers: this item is included in nep-ecm
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.cemmap.ac.uk/wp-content/uploads/2024/1 ... misspecification.pdf (application/pdf)

Related works:
Working Paper: Adapting to Misspecification (2024)
Working Paper: Adapting to Misspecification (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:18/24

DOI: 10.47004/wp.cem.2024.1824

Access Statistics for this paper

More papers in CeMMAP working papers from Institute for Fiscal Studies Contact information at EDIRC.
Bibliographic data for series maintained by Dermot Watson ().