EconPapers    
Economics at your fingertips  
 

Robust estimation with many instruments

Mikkel Sølvsten

Journal of Econometrics, 2020, vol. 214, issue 2, 495-512

Abstract: Linear instrumental variables models are widely used in empirical work, but often associated with low estimator precision. This paper proposes an estimator that is robust to outliers and shows that the estimator is minimax optimal in a class of estimators that includes the limited maximum likelihood estimator (LIML). Intuitively, this optimal robust estimator combines LIML with Winsorization of the structural residuals and the Winsorization leads to improved precision under thick-tailed error distributions. Consistency and asymptotic normality of the estimator are established under many instruments asymptotics and a consistent variance estimator which allows for asymptotically valid inference is provided.

Keywords: Instrumental variables; Generalized method of moments; Minimax estimation; Stein’s method (search for similar items in EconPapers)
JEL-codes: C26 C36 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407619301721
Full text for ScienceDirect subscribers only

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:eee:econom:v:214:y:2020:i:2:p:495-512

DOI: 10.1016/j.jeconom.2019.04.040

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2021-06-30
Handle: RePEc:eee:econom:v:214:y:2020:i:2:p:495-512