Nonparametric Prewhitening Estimators for Conditional Quantiles
We define a nonparametric prewhitening method for estimating conditional quantiles based on local linear quantile regression. We characterize the bias, variance and asymptotic normality of the proposed estimator. Under weak conditions our estimator can achieve bias reduction and have the same varian...
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sg-smu-ink.soe_research-14352018-08-31T07:20:10Z Nonparametric Prewhitening Estimators for Conditional Quantiles SU, Liangjun ULLAH, Aman We define a nonparametric prewhitening method for estimating conditional quantiles based on local linear quantile regression. We characterize the bias, variance and asymptotic normality of the proposed estimator. Under weak conditions our estimator can achieve bias reduction and have the same variance as the local linear quantile estimators. A small set of Monte Carlo simulations is carried out to illustrate the performance of our estimators. An application to US gross domestic product data demonstrates the usefulness of our methodology. 2008-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/436 https://ink.library.smu.edu.sg/context/soe_research/article/1435/viewcontent/A18n317.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Local linear quantile regression nonparametric quantile regression prediction interval prewhitening estimator weighted Nadaraya-Watson estimator Econometrics |
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Local linear quantile regression nonparametric quantile regression prediction interval prewhitening estimator weighted Nadaraya-Watson estimator Econometrics SU, Liangjun ULLAH, Aman Nonparametric Prewhitening Estimators for Conditional Quantiles |
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We define a nonparametric prewhitening method for estimating conditional quantiles based on local linear quantile regression. We characterize the bias, variance and asymptotic normality of the proposed estimator. Under weak conditions our estimator can achieve bias reduction and have the same variance as the local linear quantile estimators. A small set of Monte Carlo simulations is carried out to illustrate the performance of our estimators. An application to US gross domestic product data demonstrates the usefulness of our methodology. |
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SU, Liangjun ULLAH, Aman |
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SU, Liangjun ULLAH, Aman |
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SU, Liangjun |
title |
Nonparametric Prewhitening Estimators for Conditional Quantiles |
title_short |
Nonparametric Prewhitening Estimators for Conditional Quantiles |
title_full |
Nonparametric Prewhitening Estimators for Conditional Quantiles |
title_fullStr |
Nonparametric Prewhitening Estimators for Conditional Quantiles |
title_full_unstemmed |
Nonparametric Prewhitening Estimators for Conditional Quantiles |
title_sort |
nonparametric prewhitening estimators for conditional quantiles |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2008 |
url |
https://ink.library.smu.edu.sg/soe_research/436 https://ink.library.smu.edu.sg/context/soe_research/article/1435/viewcontent/A18n317.pdf |
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