Identification and Estimation of Simultaneous Equation Models Using Higher-Order Cumulant Restrictions

• URL: http://arxiv.org/abs/2501.06777v1
• Date: Sun, 12 Jan 2025 11:27:39 GMT
• Title: Identification and Estimation of Simultaneous Equation Models Using Higher-Order Cumulant Restrictions
• Authors: Ziyu Jiang,
• Abstract summary: Identifying structural parameters in linear simultaneous equation models is a fundamental challenge in economics and related fields. We show that under any diagonal higher-cumulant condition, the structural parameter matrix can be identified by solving an eigenvector problem. Our framework offers a transparent way to test for it, all within the same higher-orderity setting employed by earlier studies.
• Score: 9.10992754495906
• License:
• Abstract: Identifying structural parameters in linear simultaneous equation models is a fundamental challenge in economics and related fields. Recent work leverages higher-order distributional moments, exploiting the fact that non-Gaussian data carry more structural information than the Gaussian framework. While many of these contributions still require zero-covariance assumptions for structural errors, this paper shows that such an assumption can be dispensed with. Specifically, we demonstrate that under any diagonal higher-cumulant condition, the structural parameter matrix can be identified by solving an eigenvector problem. This yields a direct identification argument and motivates a simple sample-analogue estimator that is both consistent and asymptotically normal. Moreover, when uncorrelatedness may still be plausible -- such as in vector autoregression models -- our framework offers a transparent way to test for it, all within the same higher-order orthogonality setting employed by earlier studies. Monte Carlo simulations confirm desirable finite-sample performance, and we further illustrate the method's practical value in two empirical applications.

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