Department of Economics and Business Economics

Optimal model averaging of varying coefficient models

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Optimal model averaging of varying coefficient models. / Li, Cong; Li, Qi; Racine, Jeffrey S. et al.

In: Statistica Sinica, Vol. 28, No. 4, 01.10.2018, p. 2795-2809.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Li, C, Li, Q, Racine, JS & Zhang, D 2018, 'Optimal model averaging of varying coefficient models', Statistica Sinica, vol. 28, no. 4, pp. 2795-2809. https://doi.org/10.5705/ss.202017.0034

APA

Li, C., Li, Q., Racine, J. S., & Zhang, D. (2018). Optimal model averaging of varying coefficient models. Statistica Sinica, 28(4), 2795-2809. https://doi.org/10.5705/ss.202017.0034

CBE

MLA

Vancouver

Li C, Li Q, Racine JS, Zhang D. Optimal model averaging of varying coefficient models. Statistica Sinica. 2018 Oct 1;28(4):2795-2809. https://doi.org/10.5705/ss.202017.0034

Author

Li, Cong ; Li, Qi ; Racine, Jeffrey S. et al. / Optimal model averaging of varying coefficient models. In: Statistica Sinica. 2018 ; Vol. 28, No. 4. pp. 2795-2809.

Bibtex

@article{79080e2aef32437eadda83ed3c7be2a3,
title = "Optimal model averaging of varying coefficient models",
abstract = "We consider the problem of model averaging over a set of semiparametric varying coefficient models where the varying coefficients can be functions of continuous and categorical variables. We propose a Mallows model averaging procedure that is capable of delivering model averaging estimators with solid finite-sample performance. Theoretical underpinnings are provided, finite-sample performance is assessed via Monte Carlo simulation, and an illustrative application is presented. The approach is very simple to implement in practice and R code is provided as supplementary material.",
keywords = "Candidate models, Kernel smoothing, Semiparametric",
author = "Cong Li and Qi Li and Racine, {Jeffrey S.} and Daiqiang Zhang",
year = "2018",
month = oct,
day = "1",
doi = "10.5705/ss.202017.0034",
language = "English",
volume = "28",
pages = "2795--2809",
journal = "Statistica Sinica",
issn = "1017-0405",
publisher = "Academia Sinica Institute of Statistical Science",
number = "4",

}

RIS

TY - JOUR

T1 - Optimal model averaging of varying coefficient models

AU - Li, Cong

AU - Li, Qi

AU - Racine, Jeffrey S.

AU - Zhang, Daiqiang

PY - 2018/10/1

Y1 - 2018/10/1

N2 - We consider the problem of model averaging over a set of semiparametric varying coefficient models where the varying coefficients can be functions of continuous and categorical variables. We propose a Mallows model averaging procedure that is capable of delivering model averaging estimators with solid finite-sample performance. Theoretical underpinnings are provided, finite-sample performance is assessed via Monte Carlo simulation, and an illustrative application is presented. The approach is very simple to implement in practice and R code is provided as supplementary material.

AB - We consider the problem of model averaging over a set of semiparametric varying coefficient models where the varying coefficients can be functions of continuous and categorical variables. We propose a Mallows model averaging procedure that is capable of delivering model averaging estimators with solid finite-sample performance. Theoretical underpinnings are provided, finite-sample performance is assessed via Monte Carlo simulation, and an illustrative application is presented. The approach is very simple to implement in practice and R code is provided as supplementary material.

KW - Candidate models

KW - Kernel smoothing

KW - Semiparametric

UR - http://www.scopus.com/inward/record.url?scp=85054517487&partnerID=8YFLogxK

U2 - 10.5705/ss.202017.0034

DO - 10.5705/ss.202017.0034

M3 - Journal article

AN - SCOPUS:85054517487

VL - 28

SP - 2795

EP - 2809

JO - Statistica Sinica

JF - Statistica Sinica

SN - 1017-0405

IS - 4

ER -