Econometrica: Sep 2019, Volume 87, Issue 5

Nonparametric Inference on State Dependence in Unemployment
p. 1475-1505

Alexander Torgovitsky

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.

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Supplement to "Nonparametric Inference on State Dependence in Unemployment"

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Supplement to "Nonparametric Inference on State Dependence in Unemployment"

The supplemental appendix contains: (i) Discussions of extending the DPO model to discrete outcomes our higher order state dependence; (ii) A brief survey of semiparametric dynamic binary response models; (iii) Proofs for the propositions in the main text; (iv) A discussion of the linearity of parameters and assumptions used in the DPO model; (v) A discussion of dimension reduction strategies; (vi) A discussion of estimation and statistical inference; and (vii) Additional empirical estimates.

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