发表机构
Faculty of Economics, Fukuoka University（福冈大学经济学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究从观测数据估计同期双向交互的问题，提出SEM-DNN方法，利用条件协方差对角化学习相互的结构交互，通过蒙特卡罗实验和实际数据应用验证方法有效性，能更可靠恢复结构效应及研究相关反馈。
AI 中文摘要
从观测数据估计同期双向交互是困难的，因为每个结果对另一个结果而言是内生的，而灵活回归可能仅捕捉简化形式的依赖关系。本文提出了SEM-DNN，一种异方差神经联立方程估计器，可在无外部工具的情况下学习相互的结构交互。识别利用条件协方差对角化，该方法使用包含联立系统雅可比矩阵的对角高斯拟似然联合逼近非线性结构均值函数和特征依赖方差。蒙特卡罗实验表明，随着信息增加，SEM-DNN比参数、基于核和单独方程的神经替代方法更可靠地恢复结构效应。对即食谷物扫描仪数据的应用说明了该方法如何研究同期价格-销售反馈并评估识别强度、残差对角化、方差校准和优化敏感性。
英文摘要
Estimating contemporaneous bidirectional interactions from observational data is difficult because each outcome is endogenous to the other, while flexible regressions may capture only reduced-form dependence. This paper proposes SEM-DNN, a heteroscedastic neural simultaneous-equation estimator that learns reciprocal structural interactions without external instruments. Identification exploits conditional covariance diagonalization: when structural shocks have zero conditional means, are conditionally uncorrelated given predetermined covariates, and exhibit nonproportional conditional variances, only the true interaction coefficients diagonalize the conditional residual covariance across the feature space. The method jointly approximates nonlinear structural mean functions and feature-dependent variances using a diagonal Gaussian quasi-likelihood that incorporates the simultaneous-system Jacobian. We establish unique identification and positive-definite local curvature of the profiled population criterion and show that, under neural-profile compatibility conditions, the implemented neural criterion inherits this curvature despite nonunique network parameterizations. The coefficients admit a causal interpretation when the structural equations represent autonomous mechanisms that remain invariant under the relevant interventions. Monte Carlo experiments with nonlinear, high-dimensional nuisance functions and non-Gaussian shocks show that SEM-DNN recovers structural effects more reliably than parametric, kernel-based, and separate-equation neural alternatives as information increases, although at greater computational cost. An application to ready-to-eat cereal scanner data illustrates how the method can study contemporaneous price-sales feedback and assess identification strength, residual diagonalization, variance calibration, and optimization sensitivity.
