Inference on Mixtures Under Tail Restrictions

16 Pages Posted: 4 Dec 2013

See all articles by Marc Henry

Marc Henry

Pennsylvania State University

Koen Jochmans

Sciences Po - Department of Economics

Bernard Salanie

Columbia University - Graduate School of Arts and Sciences - Department of Economics; CESifo (Center for Economic Studies and Ifo Institute)

Date Written: December 3, 2013

Abstract

Two-component mixtures are nonparametrically identified under tail-dominance conditions on the component distributions if a source of variation is available that affects the mixing proportions but not the component distributions. We motivate these restrictions through several examples. One interesting example is a location model where the location parameter is subject to classical measurement error. The identification analysis suggests very simple closed-form estimators of the component distributions and mixing proportions based on ratios of intermediate quantiles. We derive their asymptotic properties using results on tail empirical processes, and we provide simulation evidence on their finite-sample performance.

Keywords: mixture model, nonparametric identification and estimation, tail empirical process

Suggested Citation

Henry, Marc and Jochmans, Koen and Salanie, Bernard, Inference on Mixtures Under Tail Restrictions (December 3, 2013). Available at SSRN: https://ssrn.com/abstract=2362910 or http://dx.doi.org/10.2139/ssrn.2362910

Marc Henry (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Koen Jochmans

Sciences Po - Department of Economics ( email )

28, rue des Saints peres
Paris, 75007
France

Bernard Salanie

Columbia University - Graduate School of Arts and Sciences - Department of Economics ( email )

420 W. 118th Street
New York, NY 10027
United States

CESifo (Center for Economic Studies and Ifo Institute)

Poschinger Str. 5
Munich, DE-81679
Germany

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