Bootstrap Inference on a Factor Model Based Average Treatment Effects Estimator

Luya Wang, Jeffrey Racine and Qiaoyu Wang

Department of Economics Working Papers from McMaster University

Abstract: We propose a novel bootstrap procedure for conducting inference for factor model based average treatment effects estimators. Our method overcomes bias inherent to existing bootstrap procedures and substantially improves upon existing large sample normal inference theory in small sample settings. The finite sample improvements arising from the use of our proposed procedure are illustrated via a set of Monte Carlo simulations, and formal justification for the procedure is outlined.

Keywords: finite sample bias; average treatment effects; bootstrap inference; factor model (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2024-05
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://socialsciences.mcmaster.ca/econ/rsrch/papers/archive/2024-03.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:mcm:deptwp:2024-03

Access Statistics for this paper

More papers in Department of Economics Working Papers from McMaster University Contact information at EDIRC.
Bibliographic data for series maintained by ().