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Inference on Estimators defined by Mathematical Programming

Yu-Wei Hsieh, Xiaoxia Shi, Matthew Shum

arXiv 26 Sep 2017 · Econometrics · publishedJournal of Econometrics (2021) · 9 citations (OpenAlex)

arXiv:1709.09115 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose an inference procedure for estimators defined by mathematical programming problems, focusing on the important special cases of linear programming (LP) and quadratic programming (QP). In these settings, the coefficients in both the objective function and the constraints of the mathematical programming problem may be estimated from data and hence involve sampling error. Our inference approach exploits the characterization of the solutions to these programming problems by complementarity conditions; by doing so, we can transform the problem of doing inference on the solution of a constrained optimization problem (a non-standard inference problem) into one involving inference based on a set of inequalities with pre-estimated coefficients, which is much better understood. We evaluate the performance of our procedure in several Monte Carlo simulations and an empirical application to the classic portfolio selection problem in finance.

Citation extraction

24
references
45
in-text mentions
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distinct cited
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Shi, X. and M. Shum (2015) Simple Two-stage Inference For A Class Of Partially Identified Models self0.693101100%
2Markowitz, H (1952) Portfolio Selection0.69351100%
3Fang, Z. and A. Santos (2016) Inference on Directionally Differentiable Functions0.58531100%
4Chiong, K. X., A. Galichon, and M. Shum (2016) Duality in Dynamic Discrete Choice Models0.51121100%
5Chiong, K., Y.-W. Hsieh, and M. Shum (2017) Counterfactual Estimation in Semiparametric Discrete Choice Models self0.51121100%
6Freyberger, J. and J. Horowitz (2015) Identification and Shape Restrictions in Nonparametric Instrumental Variables Estimation0.51121100%
7Kaido, H., F. Molinari, and J. Stoye (2016) Confidence Intervals For Projections Of Partially Identified Parameters0.51121100%
8Scherer, B (2002) Portfolio Resampling: Review and Critique0.51121100%
9Wolak, F. A (1987) An exact test for multiple inequality and equality constraints in the linear regression model0.51121100%
10Andrews, D. and P. Barwick (2012) Inference For Parameters Defined By Moment Inequalities: A Recommended Moment Selection Procedure0.40511100%

Showing the top 10 of 24 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Testing the Solvability of Systems of Linear Inequalities0.87462
2Adaptive Estimation of Aggregated Values of Conditional Linear Programs0.64441
3Inference in a class of optimization problems: Confidence regions and finite sample bounds on errors in coverage probabilities0.51121
4Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects0.51122
5Debiased Machine Learning of Set-Identified Linear Models0.40511
6Salvaging Falsified Instrumental Variable Models0.40511
7Simple subvector inference on sharp identified set in affine models0.40511
8Simple Adaptive Size-Exact Testing for Full-Vector and Subvector Inference in Moment Inequality Models0.40511
9LARGE Teacher-to-classroom assignment and student achievement LARGE0.40511
10A Computational Approach to Identification of Treatment Effects for Policy Evaluation0.40511