Inference Based on Continuous Linear Inequalities via Semi-Infinite Programming

37 Pages Posted: 6 Jun 2019

Date Written: May 18, 2019

Abstract

I develop a consistent, asymptotically normal estimator of bounds on functions of parameters partially identified by the intersection of continuous linear inequalities. The inference strategy uses results from the semi-infinite programming literature to form a convenient estimator. Aside from allowing for continuous constraints, an advantage of the estimator is that it can be used to compute a closed form confidence interval, without numerically inverting a hypothesis test. So it is easy to compute confidence intervals even if the number of parameters is very large, especially when we are interested in a linear function of parameters. I also consider the dual problem of bounding a linear function of a sequence, an infinite dimensional parameter, partially identified by finitely many linear restrictions on the sequence.

Keywords: Bounds estimation, continuous linear inequalities, monotone instrumental variables, inference on semi-infinite linear programs, consistent estimator of the value of semi-infinite programs

JEL Classification: C12

Suggested Citation

Flynn, Zach, Inference Based on Continuous Linear Inequalities via Semi-Infinite Programming (May 18, 2019). Available at SSRN: https://ssrn.com/abstract=3390788 or http://dx.doi.org/10.2139/ssrn.3390788

Zach Flynn (Contact Author)

Afiniti ( email )

1701 Pennsylvania Ave
Washington, DC 20006
United States

HOME PAGE: http://zflynn.com

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