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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 管中閔(Chung-Ming Kuan) | |
dc.contributor.author | Christos Michalopoulos | en |
dc.contributor.author | 米克里斯多斯 | zh_TW |
dc.date.accessioned | 2021-06-16T16:04:25Z | - |
dc.date.available | 2016-06-27 | |
dc.date.copyright | 2013-06-27 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-06-26 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62558 | - |
dc.description.abstract | This dissertation deals with estimation and inference of threshold quantile regression
models with one and multiple threshold values (change-points). On chapter one, we introduce the quantile regression estimation method and a nonlinear modeling approach called “threshold regression” and we motivate why it is a good idea to blend these approaches together in answering real economic data problems. On chapter 2, we formulate a threshold quantile regression model for one, known or unknown threshold value. We derive the asymptotic properties of the model parameters as well as the threshold value and develop inferential procedures to identify heterogeneous effects of different covariate quantile ranges on different quantiles of the response. We conduct inference by developing a sup-Wald test that converges to a two-parameter Gaussian process that generalizes that of Galvao et al. (2011) in allowing for serially correlated errors. In addition, we derive the limiting distribution of the estimated threshold value assuming asymptotically shrinking shifts and construct confidence intervals for the estimated threshold value via a Likelihood-ratio-type statistic. Simulation studies assess favorably our proposed methods. On chapter 3, we extend the modeling framework above to a multiple threshold quantile regression model with known or unknown threshold values and analyze the properties of the parameter estimators together with the estimated threshold values. We derive the limiting distribution of the threshold values under the asymptotic frameworks of “fixed” and “shrinking” magnitude of shifts and we discuss the case where threshold e ects on one quantile a ect neighboring quantiles as well. We develop a sup-Wald test to identify heterogeneous effects of different covariate quantile ranges on quantiles of the response and we propose a Likelihood-Ratio-type test for l versus l + 1 regime-changes in the covariate and derive its limiting distribution. Simulations assess favorably the relevance of our testing procedures. Our asymptotic results, complement and extend those of Galvao et al. (2011), Gonzalo and Pitarakis (2006) and Li and Ling (2011) to the quantile regression setting. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T16:04:25Z (GMT). No. of bitstreams: 1 ntu-102-D96323008-1.pdf: 701910 bytes, checksum: c6b1575bfcec47b6b992c14e7b0b9a18 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | List of Illustrations viii
List of Tables ix 1 Quantile Regression and Threshold-type Nonlinearities 1 1.1 Quantile Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Threshold Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Threshold Quantile Regression . . . . . . . . . . . . . . . . . . . . . 13 1.4 Technical Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2 The Quantile Regression on Quantile Ranges Model 21 2.1 The QRQR Model: Estimation and Asymptotics . . . . . . . . . . . 22 2.1.1 The model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.1.2 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.1.3 Asymptotic Properties . . . . . . . . . . . . . . . . . . . . . . 28 2.2 The limiting distribution of the partition quantile under shrinking magnitude of shifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.2.1 Confidence Intervals for the unknown threshold estimator . 41 2.3 Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.3.1 Testing under known threshold . . . . . . . . . . . . . . . . . 58 2.3.2 Testing under unknown threshold . . . . . . . . . . . . . . . 61 2.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 2.5 Technical Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3 Quantile Regression on Multiple Quantile Ranges 105 3.1 The QRMQR Model and Estimation . . . . . . . . . . . . . . . . . . 105 3.1.1 The model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 3.1.2 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 3.1.3 Asymptotic Properties . . . . . . . . . . . . . . . . . . . . . . 113 3.1.4 Threshold eects on multiple quantiles . . . . . . . . . . . . 120 3.2 Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 3.2.1 Wald Test Statistic for Multiple Regimes . . . . . . . . . . . . 122 3.2.2 Likelihood-Ratio Statistic . . . . . . . . . . . . . . . . . . . . 127 3.3 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 3.4 Technical Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Bibliography 167 | |
dc.language.iso | en | |
dc.title | 門檻分量迴歸模型之分析 | zh_TW |
dc.title | Essays in Threshold Quantile Regression | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | Yi-Ting Chen(Yi-Ting Chen),Chih-Chiang Hsu(Chih-Chiang Hsu),Yu-Chin Hsu(Yu-Chin Hsu),Hsin-Yi Lin(Hsin-Yi Lin) | |
dc.subject.keyword | quantile regression,threshold regression,single and multiple thresholds,nonlinearity test,2-parameter Gaussian process,Brownian bridge,Wald statistic,Likelihood-Ratio test,shrinking and fixed shift asymptotics, | zh_TW |
dc.relation.page | 179 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-06-26 | |
dc.contributor.author-college | 社會科學院 | zh_TW |
dc.contributor.author-dept | 經濟學研究所 | zh_TW |
顯示於系所單位: | 經濟學系 |
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