高等经济研究院教师论文被国际权威经济学期刊接收发表

2025年11月11日

近日，东北财经大学高等经济研究院宋依纯助理教授与西澳大学商学院讲师Yanli Lin合作的论文 “Addressing Endogeneity Issues in a Spatial Autoregressive Model Using Copulas” 被国际权威期刊 Journal of Econometrics 接收并线上发表。

该论文针对空间自回归（SAR）模型中由于空间权重矩阵或解释变量内生而产生的内生性问题，提出了一种全新的、无需工具变量的半参数 copula 框架。作者突破了传统高斯 copula 限制，采用带未知自由度的 Student's t Copula 构建更灵活的估计方法（涵盖高斯情形为特例），能够捕捉尾部依赖。文中提出的基于 sieve 的最大似然估计方法可同时估计结构参数、 copula 参数及边际的非参数部分，并证明该联合估计具有一致性与渐近正态性，且在半参数意义下具有效率最优性。蒙特卡洛模拟和实证分析（区域生产率溢出）均表明该方法具有较好的性能，且为那些依赖难以验证的工具变量的方法提供了一种替代。

 

Assistant Professor Yichun Song's Paper Accepted for Publication in Journal of Econometrics

 

November 11, 2025

Recently, Yichun Song, who is an assistant professor of IAER, has her paper, "Addressing Endogeneity Issues in a Spatial Autoregressive Model using Copulas" accepted for publication in Journal of Econometrics. This paper is co-authored with Yanli Lin, Lecturer in Economics at the Business School of the University of Western Australia.

 

This paper develops a new, instrument-free semiparametric copula framework for a spatial autoregressive (SAR) model to address endogeneity stemming from an endogenous spatial weights matrix, endogenous regressors, or both. Moving beyond conventional Gaussian copulas, we 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, and – 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.