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Essays on identification and estimation of treatment effects in sample selection models.

機譯：在樣本選擇模型中鑒定和評估治療效果的論文。

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This dissertation investigates identification and estimation of treatment effects with non-experimental data, using instrumental variable based Heckman-Vytlacil models. First, it reconsiders Vytlacil's (2002) result of equivalence between the Local Average Treatment Effect (LATE) framework of Imbens and Angrist (1994) and Heckman-Vytlacil latent variable selection model with a single valid instrument. Adopting a measure theoretical approach, it shows that the two may not be equivalent under a broader definition of the LATE framework, establishes equivalence conditions in this more general case, and extends the equivalence proof to models with a discrete polychotomous treatment. Second, it uses Manski's (1995) bounding argument and Imbens & Angrist's differencing argument to show that in the Heckman-Vytlacil framework a class of treatment effects represented as functionals of potential outcome distributions can be identified. These functionals include the mean, the effect on which is the average treatment effect (ATE), and the quantiles, where the correspondent effect is the quantile treatment effect (QTE). The potential outcome distributions may apply to the whole population, or be conditional on various "local" sub-populations, depending on the range of the probability of selection into treatment. However, in a model with a discrete polychotomous treatment, it shows that treatment effects cannot be point-identified. Bounds are provided for effects that are not point identified. Third, this dissertation develops a generic two-step estimation procedure, to obtain estimators for ATEs in the whole population as well as for "local" sub-populations. Then a matching estimator based on the two-step procedure is constructed to approximate the ATE, and its performance is assessed by a Monte Carlo simulation exercise. Finally, the new estimator is applied to an empirical evaluation of the college wage premium. Taking the number of siblings as an instrumental variable for college education, the two-step estimator is used to obtain point estimates of the college wage premium, which are compared with results from traditional control function methods that work in special cases of the Heckman-Vytlacil model. The results show treatment effect estimates to be sensitive to whether the instrument has a strong impact on the selection probability, and to the choice of specification.
機譯：本文利用基于工具變量的Heckman-Vytlacil模型，研究了非實驗數(shù)據(jù)對治療效果的識別和估計。首先，它用一個有效的工具重新考慮了維特拉西爾（Vytlacil）（2002）的結(jié)果，該結(jié)果等于Imbens和Angrist（1994）的本地平均治療效果（LATE）框架與Heckman-Vytlacil潛變量選擇模型之間的等效性。采用測度理論方法，它表明在LATE框架的更廣泛定義下，兩者可能不是等效的，在這種更一般的情況下建立了等效條件，并將等效證明擴展到了具有離散多變量處理的模型。其次，它使用了Manski（1995）的邊界論證和Imbens＆Angrist的微分論證來表明，在Heckman-Vytlacil框架中，可以識別出代表潛在結(jié)果分布功能的一類治療效果。這些功能包括均值，分位數(shù)，其中相應(yīng)的效果是分位數(shù)處理效果（QTE）。潛在的結(jié)果分布可能適用于整個人群，也可能取決于各種“局部”亞人群，具體取決于選擇治療的可能性范圍。但是，在具有離散多變量治療的模型中，它表明無法確定治療效果。為未點識別的效果提供了邊界。第三，本文開發(fā)了一種通用的兩步估計程序，以獲得整個人口以及“本地”亞人群的ATE估計量。然后，構(gòu)建基于兩步過程的匹配估計器以近似ATE，并通過蒙特卡洛模擬練習評估其性能。最后，將新的估算器應(yīng)用于大學工資溢價的實證評估。將兄弟姐妹的數(shù)量作為大學教育的工具變量，該兩步估計器用于獲得大學工資溢價的點估計，并與在特殊情況下適用于Heckman-Vytlacil的傳統(tǒng)控制函數(shù)方法的結(jié)果進行比較模型。結(jié)果表明，治療效果估計值對儀器是否對選擇概率和規(guī)格選擇具有重大影響很敏感。

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  • 作者

    Li, Xin.;

  • 作者單位

    The University of North Carolina at Chapel Hill.;

  • 授予單位 The University of North Carolina at Chapel Hill.;
  • 學科 Economics General.
  • 學位 Ph.D.
  • 年度 2004
  • 頁碼 131 p.
  • 總頁數(shù) 131
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類 經(jīng)濟學;
  • 關(guān)鍵詞

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