Bootstraps for Dynamic Panel Threshold Models

Woosik Gong and Myung Hwan Seo

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Abstract: This paper develops valid bootstrap inference methods for the dynamic short panel threshold regression. We demonstrate that the standard nonparametric bootstrap is inconsistent for the first-differenced generalized method of moments (GMM) estimator. The inconsistency arises from an $n^{1/4}$-consistent non-normal asymptotic distribution of the threshold estimator when the true parameter lies in the continuity region of the parameter space, which stems from the rank deficiency of the approximate Jacobian of the sample moment conditions on the continuity region. To address this, we propose a grid bootstrap to construct confidence intervals for the threshold and a residual bootstrap to construct confidence intervals for the coefficients. They are shown to be valid regardless of the model's continuity. Moreover, we establish a uniform validity for the grid bootstrap. A set of Monte Carlo experiments demonstrates that the proposed bootstraps improve upon the standard nonparametric bootstrap. An empirical application to a firm investment model illustrates our methods.

Date: 2022-11, Revised 2025-07
New Economics Papers: this item is included in nep-ecm
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