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Bayesian Models for Causal Analysis ...

Jiang, Sheng.

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Bayesian Models for Causal Analysis with Many Potentially Weak Instruments.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian Models for Causal Analysis with Many Potentially Weak Instruments./
作者:	Jiang, Sheng.
面頁冊數:	67 p.
附註:	Source: Masters Abstracts International, Volume: 54-04.
Contained By:	Masters Abstracts International54-04(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1587148
ISBN:	9781321704792

Bayesian Models for Causal Analysis with Many Potentially Weak Instruments.
Jiang, Sheng.

Bayesian Models for Causal Analysis with Many Potentially Weak Instruments. - 67 p.

Source: Masters Abstracts International, Volume: 54-04.

Thesis (M.S.)--Duke University, 2015.

This paper investigates Bayesian instrumental variable models with many instruments. The number of instrumental variables grows with the sample size and is allowed to be much larger than the sample size. With some sparsity condition on the coefficients on the instruments, we characterize a general prior specification where the posterior consistency of the parameters is established and calculate the corresponding convergence rate. In particular, we show the posterior consistency for a class of spike and slab priors on the many potentially weak instruments. The spike and slab prior shrinks the number of instrumental variables, which avoids overfitting and provides uncertainty quantifications on the first stage. A simulation study is conducted to illustrate the convergence notion and estimation/selection performance under dependent instruments. Computational issues related to the Gibbs sampler are also discussed.

ISBN: 9781321704792Subjects--Topical Terms:

517247
Statistics.

Bayesian Models for Causal Analysis with Many Potentially Weak Instruments.
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500 $a Adviser: Surya Tokdar.
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520 $a This paper investigates Bayesian instrumental variable models with many instruments. The number of instrumental variables grows with the sample size and is allowed to be much larger than the sample size. With some sparsity condition on the coefficients on the instruments, we characterize a general prior specification where the posterior consistency of the parameters is established and calculate the corresponding convergence rate. In particular, we show the posterior consistency for a class of spike and slab priors on the many potentially weak instruments. The spike and slab prior shrinks the number of instrumental variables, which avoids overfitting and provides uncertainty quantifications on the first stage. A simulation study is conducted to illustrate the convergence notion and estimation/selection performance under dependent instruments. Computational issues related to the Gibbs sampler are also discussed.
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