NONSTANDARD QUANTILE-REGRESSION INFERENCE

Autor:	S.C. Goh, Keith Knight
Rok vydání:	2009
Předmět:	Economics and Econometrics Studentization Resampling Econometrics Inference Context (language use) Conditional probability distribution Social Sciences (miscellaneous) Smoothing Quantile regression Quantile Mathematics
Zdroj:	Econometric Theory. 25:1415-1432
ISSN:	1469-4360 0266-4666
DOI:	10.1017/s0266466609090719
Popis:	It is well known that conventional Wald-type inference in the context of quantile regression is complicated by the need to construct estimates of the conditional densities of the response variables at the quantile of interest. This note explores the possibility of circumventing the need to construct conditional density estimates in this context with scale statistics that are explicitly inconsistent for the underlying conditional densities. This method of studentization leads conventional test statistics to have limiting distributions that are nonstandard but have the convenient feature of depending explicitly on the user’s choice of smoothing parameter. These limiting distributions depend on the distribution of the conditioning variables but can be straightforwardly approximated by resampling.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7237dd05375d48d880e293c9ff0e0d0e https://doi.org/10.1017/s0266466609090719 Zobrazit plný text záznamu