<p>Extremal quantile index is a concept that the quantile index will drift to zero (or one)</p><p>as the sample size increases. The three chapters of my dissertation consists of three</p><p>applications of this concept in three distinct econometric problems. In Chapter 2, I</p><p>use the concept of extremal quantile index to derive new asymptotic properties and</p><p>inference method for quantile treatment effect estimators when the quantile index</p><p>of interest is close to zero. In Chapter 3, I rely on the concept of extremal quantile</p><p>index to achieve identification at infinity of the sample selection models and propose</p><p>a new inference method. Last, in Chapter 4, I use the concept of extremal quantile</p><p>index to define an asymptotic trimming scheme which can be used to control the</p><p>convergence rate of the estimator of the intercept of binary response models.</p> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/12160 |
| Date | January 2016 |
| Creators | Zhang, Yichong |
| Contributors | Khan, Shakeeb, Maurel, Arnaud |
| Source Sets | Duke University |
| Detected Language | English |
| Type | Dissertation |
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