Smooth Minimum Distance Estimation and Testing with Conditional Estimating Equations: Uniform in Bandwidth Theory

Pascal Lavergne and Valentin Patilea

No 13-404, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: We study the influence of a bandwidth parameter in inference with conditional estimating equations. In that aim, we propose a new class of smooth minimum distance estimators and we develop a theory that focuses on uniformity in bandwidth. We establish a vn-asymptotic representation of our estimator as a process indexed by a bandwidth that can vary within a wide range including bandwidths independent of the sample size. We develop an efficient version of our estimator. We also study its behavior in misspecified models. We develop a procedure based on a distance metric statistic for testing restrictions on parameters as well as a bootstrap technique to account for the bandwidth’s influence. Our new methods are simple to implement, apply to non-smooth problems, and perform well in our simulations.

Keywords: Semiparametric Estimation; Conditional Estimating Equations; Smoothing Methods; Asymptotic Efficiency; Hypothesis Testing; Bootstrap (search for similar items in EconPapers)
JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 2013-03
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (28)

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Journal Article: Smooth minimum distance estimation and testing with conditional estimating equations: Uniform in bandwidth theory (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:27219

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