|This centre is a member of The LSE Research Laboratory [RLAB]: CASE | CVER | CEP | SERC | STICERD||Cookies?|
Paper No' EM/589: | Full paper
Save Reference as: BibTeX File | EndNote Import File
Keywords: Cube root asymptotics, Maximal inequality, Mixing process, Partial identification, Parameter-dependent localization
JEL Classification: C12; C14
Is hard copy/paper copy available? YES - Paper Copy Still In Print.
This Paper is published under the following series: Econometrics
Share this page: Google Bookmarks | Facebook | Twitter
Abstract:This paper examines asymptotic properties of local M-estimators under three sets of high-level conditions. These conditions are sufficiently general to cover the minimum volume predictive region, conditional maximum score estimator for a panel data discrete choice model, and many other widely used estimators in statistics and econometrics. Specifically, they allow for discontinuous criterion functions of weakly dependent observations, which may be localized by kernel smoothing and contain nuisance parameters whose dimension may grow to infinity. Furthermore, the localization can occur around parameter values rather than around a fixed point and the observation may take limited values, which leads to set estimators. Our theory produces three different nonparametric cube root rates and enables valid inference for the local M-estimators, building on novel maximal inequalities for weakly dependent data. Our results include the standard cube root asymptotics as a special case. To illustrate the usefulness of our results, we verify our conditions for various examples such as the Hough transform estimator with diminishing bandwidth, maximum score-type set estimator, and many others.
Copyright © RLAB & LSE 2003 - 2018 | LSE, Houghton Street, London WC2A 2AE | Contact: RLAB | Site updated 24 May 2018