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Bayesian Models for Causal Analysis with Many Potentially Weak Instruments

dc.contributor.advisor Tokdar, Surya Tapas
dc.contributor.author Jiang, Sheng
dc.date.accessioned 2015-05-12T20:50:48Z
dc.date.available 2015-05-12T20:50:48Z
dc.date.issued 2015
dc.identifier.uri https://hdl.handle.net/10161/10020
dc.description.abstract <p>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. </p><p>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.</p>
dc.subject Statistics
dc.subject Economics
dc.subject Bayesian methods
dc.subject High dimensionality
dc.subject instrumental variable
dc.subject posterior consistency
dc.subject Sparsity
dc.subject variable selection
dc.title Bayesian Models for Causal Analysis with Many Potentially Weak Instruments
dc.type Master's thesis
dc.department Statistical and Economic Modeling


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