Structural Models for Policy-Making: Coping with Parametric Uncertainty
Philipp Eisenhauer (),
Janos Gabler and
Lena Janys ()
Additional contact information
Philipp Eisenhauer: University of Bonn
Janos Gabler: IZA
Lena Janys: Newcastle University
No 14317, IZA Discussion Papers from Institute of Labor Economics (IZA)
Abstract:
The ex-ante evaluation of policies using structural econometric models is based on estimated parameters as a stand-in for the truth. This practice ignores uncertainty in the counterfactual policy predictions of the model. We develop a generic approach that deals with parametric uncertainty using uncertainty sets and frames model-informed policymaking as a decision problem under uncertainty. The seminal human capital investment model by Keane and Wolpin (1997) provides us with a well-known, influential, and empirically-grounded test case. We document considerable uncertainty in their policy predictions and highlight the resulting policy recommendations from using different formal rules on decision-making under uncertainty.
Keywords: uncertainty quantification; structural estimation; statistical decision rules (search for similar items in EconPapers)
JEL-codes: C44 C54 D81 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2021-04
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://docs.iza.org/dp14317.pdf (application/pdf)
Related works:
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:iza:izadps:dp14317
Ordering information: This working paper can be ordered from
IZA, Margard Ody, P.O. Box 7240, D-53072 Bonn, Germany
Access Statistics for this paper
More papers in IZA Discussion Papers from Institute of Labor Economics (IZA) IZA, P.O. Box 7240, D-53072 Bonn, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Holger Hinte ().