Nonparametric Significance Testing in Measurement Error Models

Hao Dong () and Luke Taylor

No 2003, Departmental Working Papers from Southern Methodist University, Department of Economics

Abstract: We develop the first nonparametric significance test for regression models with classical measurement error in the regressors. In particular, the Cram�r-von Mises test and the Kolmogorov-Smirnov test for the null hypothesis $E[Y|X^{*},Z^{*}]=E[Y|Z^{*}]$ 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 root-n rate. We also highlight the finite sample performance of the test through a Monte Carlo study.

Keywords: Significance test; deconvolution; classical measurement error; unknown error distribution. (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2020-02
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: NONPARAMETRIC SIGNIFICANCE TESTING IN MEASUREMENT ERROR MODELS (2022)
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