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Nonparametric Inference on State Dependence in Unemployment
Author(s) -
Torgovitsky Alexander
Publication year - 2019
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta14138
Subject(s) - survey of income and program participation , nonparametric statistics , unemployment , econometrics , inference , state (computer science) , construct (python library) , economics , mathematics , computer science , statistics , algorithm , artificial intelligence , economic growth , programming language
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.