Sparse models and methods for optimal instruments with an application to eminent domain
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In this paper, preliminary results of this paper were presented at Chernozhukov's invited Cowles Foundation lecture at the Northern American meetings of the Econometric society in June of 2009.Abstract:
Date: First version: June 2009, this version October 28, 2010. Preliminary results of this paper were FIRST presented at Chernozhukov's invited Cowles Foundation lecture at the Northern American meetings of the Econometric society in June of 2009. We thank seminar participants at Brown, Columbia, Harvard-MIT, the Dutch Econometric Study Group, Fuqua School of Business, and NYU for helpful comments. We also thank Denis Chetverikov, JB Doyle, and Joonhwan Lee for thorough reading of this paper and helpful feedback.read more
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Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain
TL;DR: A fully data-driven method for choosing the user-specified penalty that must be provided in obtaining LASSO and Post-LASSO estimates is provided and its asymptotic validity under non-Gaussian, heteroscedastic disturbances is established.
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High-Dimensional Methods and Inference on Structural and Treatment Effects
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References
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Testing for Weak Instruments in Linear IV Regression
James H. Stock,Motohiro Yogo +1 more
TL;DR: This paper proposed quantitative definitions of weak instruments based on the maximum IV estimator bias, or the maximum Wald test size distortion, when there are multiple endogenous regressors, and tabulated critical values that enable using the first-stage F-statistic (or, for instance, the Cragg-Donald (1993) statistic) to test whether give n instruments are weak.
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The Dantzig selector: Statistical estimation when p is much larger than n
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: In many important statistical applications, the number of variables or parameters p is much larger than the total number of observations n as discussed by the authors, and it is possible to estimate β reliably based on the noisy data y.
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