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- W3168648694 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 $Eleft [Y|X^{*},Z^{*}right ]=Eleft [Y|X^{*}right ]$ 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." @default.
- W3168648694 created "2021-06-22" @default.
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- W3168648694 date "2021-06-04" @default.
- W3168648694 modified "2023-09-25" @default.
- W3168648694 title "NONPARAMETRIC SIGNIFICANCE TESTING IN MEASUREMENT ERROR MODELS" @default.
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- W3168648694 doi "https://doi.org/10.1017/s0266466621000220" @default.
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