Nonparametric Identification

Katherine Hauck () and Tiemen Woutersen ()
Additional contact information
Katherine Hauck: University of California, Davis
Tiemen Woutersen: University of Arizona

A chapter in Teaching Econometrics, 2026, pp 197-206 from Springer

Abstract: Abstract Using a nonparametric model allows empirical researchers to more closely align their research design to economic theory and to obtain more robust results. We show the importance of nonparametric identification by contrasting it with parametric identification. In particular, we show the numerical instability of the estimated parameters in a model that is parametrically identified but fails to be nonparametrically identified, and we demonstrate this lack of identification with a proof.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:adschp:978-3-031-97942-2_11

Ordering information: This item can be ordered from
http://www.springer.com/9783031979422

DOI: 10.1007/978-3-031-97942-2_11

Access Statistics for this chapter

More chapters in Advanced Studies in Theoretical and Applied Econometrics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().