Oracally efficient estimation and specification testing of partially linear additive spatial autoregressive models

Shiyuan Chen, Xiaojun Song and Jihai Yu

Econometric Reviews, 2025, vol. 44, issue 8, 1120-1143

Abstract: This article proposes a computationally efficient sieve-based generalized method of moments (GMM) estimator for partially linear additive spatial autoregressive models, incorporating both linear and quadratic moment conditions. The proposed GMM estimators remain valid even when all regressors are irrelevant and are shown to be consistent and asymptotically normal. To improve the convergence rate for each nonparametric additive component, we propose a more efficient two-stage estimation procedure and establish its asymptotic validity and oracle property. Additionally, we develop Lagrange multiplier (LM)-type specification tests to assess the significance and functional forms of the nonparametric additive components. These LM tests asymptotically follow a standard normal distribution after appropriate centering and scaling under the null hypotheses. Simulation studies show that the proposed GMM estimators, two-stage estimation, and LM tests perform well in finite samples. Applying our estimation and testing methods to the Boston housing data, we find strong spatial dependence in housing prices and significant interaction effects, as captured by the partially linear additive spatial autoregressive model.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2025.2486989 (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:8:p:1120-1143

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

DOI: 10.1080/07474938.2025.2486989

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 ().