Robust Hypothesis Tests for M-Estimators with Possibly Non-differentiable Estimating Functions

Wei-Ming Lee, Yu-Chin Hsu () and Chung-Ming Kuan
Additional contact information
Wei-Ming Lee: Department of Economics National Chung Cheng University, http://econ.ccu.edu.tw/
Yu-Chin Hsu: Institute of Economics, Academia Sinica, Taipei, Taiwan, https://www.econ.sinica.edu.tw/
Chung-Ming Kuan: Department of Finance National Taiwan University

No 14-A004, IEAS Working Paper : academic research from Institute of Economics, Academia Sinica, Taipei, Taiwan

Abstract: We propose a new robust hypothesis test for (possibly nonlinear) constraints on Mestimators with possibly non-differentiable estimating functions. The proposed test employs a random normalizing matrix computed from recursive M-estimators to eliminate the nuisance parameters arising from the asymptotic covariance matrix. It does not require consistent estimation of any nuisance parameters, in contrast with the conventional heteroskedasticity autocorrelation consistent (HAC)-type test and the KVB-type test of Kiefer, Vogelsang, and Bunzel (2000). Our test reduces to the KVB-type test in simple location models with OLS estimation, so the error in rejection probability of our test in a Gaussian location model is OIP(T−1 log T). We discuss robust testing in quantile regression, and censored regression models in details. In simulation studies, we find that our test has better size control and better finite sample power than the HAC-type and KVB-type tests.

Keywords: censored regression; generalized method of moments; robust hypothesis testing; KVB approach; M-estimator; quantile regression (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2014-03, Revised 2014-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Robust hypothesis tests for M‐estimators with possibly non‐differentiable estimating functions (2015)
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