Work

Bootstrapping Kernel-Based Semiparametric Estimators

Year: 2014

Type: preprint

Abstract: This paper develops alternative asymptotic results for a large class of two-step semiparametric estimators. The first main result is an asymptotic distribution result for such estimators and differs from those obtained in earlier work on classes of semiparametric two-step estimators by accommodating a non-negligible bias. A noteworthy feature of the assumptions under which the result is obtained is that reliance on a commonly employed stochastic equicontinuity condition is avoided. The second main result shows that the bootstrap provides an automatic method of correcting for the bias even when it is non-negligible.

Source: RePEc: Research Papers in Economics

Authors: Matias D. Cattaneo, Michael Jansson

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Research Foundation, Danmarks Grundforskningsfond

Awards: Center for Research in Econometric Analysis of Time Series (CREATES), Collaborative Research: Non-Standard Asymptotic Theory for Semiparametric Estimators, Collaborative Research: Non-Standard Asymptotic Theory for Semiparametric Estimators

OpenAlex ID: W302951386

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Cited by: 7

Related to: 8