An efficient residual-adjusted two-step estimator for a SARAR model

Lung-Fei Lee, Yanli Lin and Yang Yang

Econometric Reviews, 2025, vol. 44, issue 7, 886-914

Abstract: This article proposes an efficient two-step estimator for a spatial autoregressive (SAR) model with SAR disturbances (SARAR). By leveraging the residual-adjusted estimation framework of Hatanaka (1974, 1976) and Dhrymes (1974), our estimator achieves asymptotic efficiency comparable to the quasi-maximum likelihood estimator (QMLE) or the best generalized method of moments estimator (BGMME) in Liu, Lee, and Bollingerm (2010), while significantly reducing computational complexity and execution time. Monte Carlo simulations demonstrate the superior performance of our numerical procedure across both small and relatively large sample sizes. An empirical application to U.S. county-level homicide data reveals significant positive spatial spillover effects, highlighting the critical need for multi-regional collaboration in crime prevention and economic development policies to reduce homicide rates.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2025.2458226 (text/html)
Access to full text is restricted to subscribers.

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:taf:emetrv:v:44:y:2025:i:7:p:886-914

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474938.2025.2458226

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().