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- Title
Robust hypothesis tests for M-estimators with possibly non-differentiable estimating functions.
- Authors
Lee, Wei‐Ming; Hsu, Yu‐Chin; Kuan, Chung‐Ming
- Abstract
We propose a new robust hypothesis test for (possibly non-linear) constraints on M-estimators 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 heteroscedasticity-autocorrelation consistent (HAC)-type test and the Kiefer-Vogelsang-Bunzel (KVB)-type test. Our test reduces to the KVB-type test in simple location models with ordinary least-squares estimation, so the error in the rejection probability of our test in a Gaussian location model is
- Subjects
ROBUST control; NONDIFFERENTIABLE functions; HYPOTHESIS; HETEROSCEDASTICITY; AUTOCORRELATION (Statistics); PARAMETER estimation
- Publication
Econometrics Journal, 2015, Vol 18, Issue 1, p95
- ISSN
1368-4221
- Publication type
Article
- DOI
10.1111/ectj.12041