Addressing Endogeneity Issues in a Spatial Autoregressive Model using Copulas

Yanli Lin and Yichun Song
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Yanli Lin: Economics Programme, University of Western Australia, http://www.web.uwa.edu.au/person/yanli.lin
Yichun Song: Center for Industrial and Business Organization and Institute for Advanced Economic Research, Dongbei University of Finance and Economics

No 25-07, Economics Discussion / Working Papers from The University of Western Australia, Department of Economics

Abstract: This paper develops a new, instrument-free semi-parametric 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 (MLE) that jointly estimates all structural, copula, and non-parametric 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

Keywords: Spatial autoregressive model; Endogenous spatial weights matrix; Endogenous regressors; Copula method; Sieve maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: C31 C51 (search for similar items in EconPapers)
Pages: 62
Date: 2025
New Economics Papers: this item is included in nep-ecm and nep-ure
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