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The smooth colonel and the reverend find common ground

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The smooth colonel and the reverend find common ground. / Kiefer, Nicholas M.; Racine, Jeffrey S.

In: Econometric Reviews, Vol. 36, No. 1-3, 03.2017, p. 241-256.

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Kiefer NM, Racine JS. The smooth colonel and the reverend find common ground. Econometric Reviews. 2017 Mar;36(1-3):241-256. doi: 10.1080/07474938.2015.1114304

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Kiefer, Nicholas M. ; Racine, Jeffrey S. / The smooth colonel and the reverend find common ground. In: Econometric Reviews. 2017 ; Vol. 36, No. 1-3. pp. 241-256.

Bibtex

@article{e0a455b480684f89b756162ccb3b44b5,
title = "The smooth colonel and the reverend find common ground",
abstract = "A semiparametric regression estimator that exploits categorical (i.e., discrete-support) kernel functions is developed for a broad class of hierarchical models including the pooled regression estimator, the fixed-effects estimator familiar from panel data, and the varying coefficient estimator, among others. Separate shrinking is allowed for each coefficient. Regressors may be continuous or discrete. The estimator is motivated as an intuitive and appealing generalization of existing methods. It is then supported by demonstrating that it can be realized as a posterior mean in the Lindley and Smith (1972) framework. As a demonstration of the flexibility of the proposed approach, the model is extended to nonparametric hierarchical regression based on B-splines.",
keywords = "Bayesian methods, econometrics, hierarchical models, kernel estimation, nonparametrics, panel data",
author = "Kiefer, {Nicholas M.} and Racine, {Jeffrey S.}",
year = "2017",
month = mar,
doi = "10.1080/07474938.2015.1114304",
language = "English",
volume = "36",
pages = "241--256",
journal = "Econometric Reviews",
issn = "0747-4938",
publisher = "Taylor & Francis Inc.",
number = "1-3",

}

RIS

TY - JOUR

T1 - The smooth colonel and the reverend find common ground

AU - Kiefer, Nicholas M.

AU - Racine, Jeffrey S.

PY - 2017/3

Y1 - 2017/3

N2 - A semiparametric regression estimator that exploits categorical (i.e., discrete-support) kernel functions is developed for a broad class of hierarchical models including the pooled regression estimator, the fixed-effects estimator familiar from panel data, and the varying coefficient estimator, among others. Separate shrinking is allowed for each coefficient. Regressors may be continuous or discrete. The estimator is motivated as an intuitive and appealing generalization of existing methods. It is then supported by demonstrating that it can be realized as a posterior mean in the Lindley and Smith (1972) framework. As a demonstration of the flexibility of the proposed approach, the model is extended to nonparametric hierarchical regression based on B-splines.

AB - A semiparametric regression estimator that exploits categorical (i.e., discrete-support) kernel functions is developed for a broad class of hierarchical models including the pooled regression estimator, the fixed-effects estimator familiar from panel data, and the varying coefficient estimator, among others. Separate shrinking is allowed for each coefficient. Regressors may be continuous or discrete. The estimator is motivated as an intuitive and appealing generalization of existing methods. It is then supported by demonstrating that it can be realized as a posterior mean in the Lindley and Smith (1972) framework. As a demonstration of the flexibility of the proposed approach, the model is extended to nonparametric hierarchical regression based on B-splines.

KW - Bayesian methods

KW - econometrics

KW - hierarchical models

KW - kernel estimation

KW - nonparametrics

KW - panel data

UR - http://www.scopus.com/inward/record.url?scp=84961211588&partnerID=8YFLogxK

U2 - 10.1080/07474938.2015.1114304

DO - 10.1080/07474938.2015.1114304

M3 - Journal article

AN - SCOPUS:84961211588

VL - 36

SP - 241

EP - 256

JO - Econometric Reviews

JF - Econometric Reviews

SN - 0747-4938

IS - 1-3

ER -