Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series-Reference-Cited by-同舟云学术

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Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series

Published:2018-04-03 Issue:522 Volume:113 Page:919-932
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Chen Jia¹,Li Degui²,Linton Oliver³,Lu Zudi⁴

Affiliation:

1. Department of Economics and Related Studies, University of York, York, United Kingdom

2. Department of Mathematics, University of York, York, United Kingdom

3. Faculty of Economics, University of Cambridge, Cambridge, United Kingdom

4. Statistical Sciences Research Institute and School of Mathematical Sciences, University of Southampton, Southampton, United Kingdom

Funder

National Institutes of Health

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Office of Naval Research

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2017.1302339

Reference41 articles.

1. A Bayesian extension of the minimum AIC procedure of autoregressive model fitting

2. A Model-Averaging Approach for High-Dimensional Regression

3. Determining the Number of Factors in Approximate Factor Models

4. Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach

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