Wald inference on varying coefficients

Abhimanyu Gupta, Xi Qu, Sorawoot Srisuma and Jiajun Zhang

Papers from arXiv.org

Abstract: We present simple to implement Wald-type statistics that deliver a general nonparametric inference theory for linear restrictions on varying coefficients in a range of regression models allowing for cross-sectional or spatial dependence. We provide a general central limit theorem that covers a broad range of error spatial dependence structures, allows for a degree of misspecification robustness via nonparametric spatial weights and permits inference on both varying regression and spatial dependence parameters. Using our method, we first uncover evidence of constant returns to scale in the Chinese nonmetal mineral industry's production function, and then show that Boston house prices respond nonlinearly to proximity to employment centers. A simulation study confirms that our tests perform very well in finite samples.

Date: 2025-02, Revised 2025-09
New Economics Papers: this item is included in nep-ecm and nep-ure
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