Recently, Song Yichun, an assistant professor at the Institute for Advanced Economic Research (IAER) of Dongbei University of Finance and Economics (DUFE), has had her paper “Addressing endogeneity issues in a spatial autoregressive model using copulas” accepted for publication in the Journal of Econometrics [https://doi.org/10.1016/j.jeconom.2025.106106]. The paper is co-authored with Yanli Lin, a lecturer at the UWA Business School.

This paper develops a novel, instrument-free semiparametric copula framework for a spatial autoregressive (SAR) model to address endogeneity arising from an endogenous spatial weights matrix, endogenous regressors, or both. Moving beyond conventional Gaussian copulas, the authors develop a flexible estimator based on the Student’s t copula with an unknown degrees-of-freedom (df) parameter, which nests the Gaussian case and allows the data to reveal the presence of tail dependence. We propose a sieve maximum likelihood estimator (SMLE) that jointly estimates all structural, copula, and nonparametric marginal parameters, and establish that this joint estimator is consistent, asymptotically normal, an—unlike prevailing multi-stage copula-correction methods—semiparametrically efficient. Monte Carlo simulations underscore the flexibility of our approach, showing that copula misspecification inflates bias and variance, whereas joint estimation improves efficiency. In an empirical application to regional productivity spillovers, we find evidence of tail dependence and demonstrate that our method offers a credible alternative to approaches that rely on hard-to-verify excluded instruments.

Written by: Song Yichun, Wang Jie

Source: Institute for Advanced Economic Research