Jackknife model averaging for quantile regressions
In this paper we consider model averaging for quantile regressions (QR) when all models under investigation are potentially misspecified and the number of parameters is diverging with the sample size. To allow for the dependence between the error terms and regressors in the QR models, we propose a j...
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sg-smu-ink.soe_research-28732020-03-31T05:19:11Z Jackknife model averaging for quantile regressions LU, Xun SU, Liangjun In this paper we consider model averaging for quantile regressions (QR) when all models under investigation are potentially misspecified and the number of parameters is diverging with the sample size. To allow for the dependence between the error terms and regressors in the QR models, we propose a jackknife model averaging (JMA) estimator which selects the weights by minimizing a leave-one-out cross-validation criterion function and demonstrate its asymptotic optimality in terms of minimizing the out-of-sample final prediction error. We conduct simulations to demonstrate the finite-sample performance of our estimator and compare it with other model selection and averaging methods. We apply our JMA method to forecast quantiles of excess stock returns and wages. (C) 2015 Elsevier B.V. All rights reserved. 2015-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1873 info:doi/10.1016/j.jeconom.2014.11.005 https://ink.library.smu.edu.sg/context/soe_research/article/2873/viewcontent/JackknifeModelAveragingQuantileRegressions_pp.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Final prediction error High dimensionality Model averaging Model selection Misspecification Quantile regression Econometrics |
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Final prediction error High dimensionality Model averaging Model selection Misspecification Quantile regression Econometrics LU, Xun SU, Liangjun Jackknife model averaging for quantile regressions |
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In this paper we consider model averaging for quantile regressions (QR) when all models under investigation are potentially misspecified and the number of parameters is diverging with the sample size. To allow for the dependence between the error terms and regressors in the QR models, we propose a jackknife model averaging (JMA) estimator which selects the weights by minimizing a leave-one-out cross-validation criterion function and demonstrate its asymptotic optimality in terms of minimizing the out-of-sample final prediction error. We conduct simulations to demonstrate the finite-sample performance of our estimator and compare it with other model selection and averaging methods. We apply our JMA method to forecast quantiles of excess stock returns and wages. (C) 2015 Elsevier B.V. All rights reserved. |
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LU, Xun SU, Liangjun |
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LU, Xun SU, Liangjun |
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LU, Xun |
title |
Jackknife model averaging for quantile regressions |
title_short |
Jackknife model averaging for quantile regressions |
title_full |
Jackknife model averaging for quantile regressions |
title_fullStr |
Jackknife model averaging for quantile regressions |
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Jackknife model averaging for quantile regressions |
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jackknife model averaging for quantile regressions |
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Institutional Knowledge at Singapore Management University |
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2015 |
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https://ink.library.smu.edu.sg/soe_research/1873 https://ink.library.smu.edu.sg/context/soe_research/article/2873/viewcontent/JackknifeModelAveragingQuantileRegressions_pp.pdf |
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