Inference for Parameters Identified by Conditional Moment Restrictions Using a Generalized Bierens Maximum Statistic

Xiaohong Chen; Sokbae Lee; Myung Hwan Seo; Myunghyun Song

doi:10.1162/rest_a_01550

Inference for Parameters Identified by Conditional Moment Restrictions Using a Generalized Bierens Maximum Statistic

Xiaohong Chen¹; Sokbae Lee²; Myung Hwan Seo³; Myunghyun Song⁴

¹ Yale University and Cowles Foundation for Research in Economics [email protected] · ² Columbia University and Centre for Microdata Methods and Practice [email protected] · ³ Seoul National University and Institute of Economic Research · ⁴ Columbia University [email protected]

The Review of Economics and Statistics 2025

Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions. We introduce a novel inference method without any prior information about which conditioning instruments are weak or irrelevant. Building on Bierens (1990), we propose penalized maximum statistics and combine bootstrap inference with model selection. Our method optimizes asymptotic power by solving a data-dependent max-min problem for tuning parameter selection. Extensive Monte Carlo experiments, based on an empirical example, demonstrate the extent to which our inference procedure is superior to those available in the literature. [C12, C36].

DOI: 10.1162/rest_a_01550
Pages: 1-45
Language: en
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