Nonparametric Inference on State Dependence in Unemployment

69 Pages Posted: 12 Feb 2019

Multiple version iconThere are 2 versions of this paper

Date Written: January 2, 2019

Abstract

This paper is about measuring state dependence in dynamic discrete outcomes. I develop a nonparametric dynamic potential outcomes (DPO) model and propose an array of parameters and identifying assumptions that can be considered in this model. I show how to construct sharp identified sets under combinations of identifying assumptions by using a flexible linear programming procedure. I apply the analysis to study state dependence in unemployment for working age high school educated men using an extract from the 2008 Survey of Income and Program Participation (SIPP). Using only nonparametric assumptions, I estimate that state dependence accounts for at least 30-40% of the four-month persistence in unemployment among high school educated men. 

Keywords: state dependence, unemployment, nonparametric, partial identification, linear programming, dynamic discrete choice, moment inequalities

JEL Classification: C14; C20; C51; J2; J6

Suggested Citation

Torgovitsky, Alexander, Nonparametric Inference on State Dependence in Unemployment (January 2, 2019). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2019-11. Available at SSRN: https://ssrn.com/abstract=3332088 or http://dx.doi.org/10.2139/ssrn.3332088

Alexander Torgovitsky (Contact Author)

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

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