NONPARAMETRIC SIGNIFICANCE TESTING IN MEASUREMENT ERROR MODELS

Hao Dong () and Luke Taylor

Econometric Theory, 2022, vol. 38, issue 3, 454-496

Abstract: We develop the first nonparametric significance test for regression models with classical measurement error in the regressors. In particular, a Cramér-von Mises test and a Kolmogorov–Smirnov test for the null hypothesis $E\left [Y|X^{*},Z^{*}\right ]=E\left [Y|X^{*}\right ]$ are proposed when only noisy measurements of $X^{*}$ and $Z^{*}$ are available. The asymptotic null distributions of the test statistics are derived, and a bootstrap method is implemented to obtain the critical values. Despite the test statistics being constructed using deconvolution estimators, we show that the test can detect a sequence of local alternatives converging to the null at the $\sqrt {n}$ -rate. We also highlight the finite sample performance of the test through a Monte Carlo study.

Date: 2022
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Working Paper: Nonparametric Significance Testing in Measurement Error Models (2020)
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