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Sieve Tail Index Regression
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 이석배 | - |
dc.contributor.author | 박다희 | - |
dc.date.accessioned | 2017-07-19T12:35:53Z | - |
dc.date.available | 2017-07-19T12:35:53Z | - |
dc.date.issued | 2014-02 | - |
dc.identifier.other | 000000018750 | - |
dc.identifier.uri | https://hdl.handle.net/10371/134626 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 경제학부, 2014. 2. 이석배. | - |
dc.description.abstract | The tail index is of particular interest since it gauges extreme behaviors in the heavy-tailed analysis. We consider estimating the tail index 'alpha' when covariate information is available under the Pareto-type distribution. We employ an exponential link function, which is partially linear, in order to associate a response variable with explanatory variables. Semi-nonparametric models are more robust but less sensitive to any specification issue compared to parametric cases. However, the unknown nonparametric parts induce other difficulties such as noncompact parameter spaces and ill-posed criterion problems in the semi-nonparametric models. The method of sieves resolves the complications by replacing the infinitedimensional parameter spaces with the compact finite-dimensional sieve spaces. It is easy and flexible to practice. We particularly study sieve maximum likelihood estimation and show the consistency of the estimators. Several conditions should be checked to insure consistency as standard asymptotic theory for parametric approach is not applicable. | - |
dc.description.tableofcontents | 1. Introduction
2. Sieve Maximum Likelihood Estimator 2.1 The Pareto-type Model 2.2 Sieve Maximum Likelihood Estimators 2.2.1 Criterion Functions in Semi-nonparametric Models 2.2.2 The 'Sieve' Maximum Likelihood Estimator 2.2.3 Choice of Sieve parameter Spaces 2.2.4 Basis Function Selection 2.3 Sample Fraction Selection 3. Theoretical Properties 3.1 Conditions for Consistency 4. Concluding Remark 5. Appendix : PROOFS | - |
dc.format | application/pdf | - |
dc.format.extent | 2623868 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | the tail Index | - |
dc.subject | semi-nonparametric | - |
dc.subject | sieve | - |
dc.subject | Pareto distribution | - |
dc.subject.ddc | 330 | - |
dc.title | Sieve Tail Index Regression | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.citation.pages | 26 | - |
dc.contributor.affiliation | 사회과학대학 경제학부 | - |
dc.date.awarded | 2014-02 | - |
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