Identification of Dynamic Panel Logit Models with Fixed Effects

Fuente: arXiv
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Autori principali: Dobronyi, Christopher, Gu, Jiaying, Kim, Kyoo il, Russell, Thomas M.
Natura: Preprint
Pubblicazione: 2021
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author Dobronyi, Christopher
Gu, Jiaying
Kim, Kyoo il
Russell, Thomas M.
author_facet Dobronyi, Christopher
Gu, Jiaying
Kim, Kyoo il
Russell, Thomas M.
contents We show that identification in a general class of dynamic panel logit models with fixed effects is related to the truncated moment problem from the mathematics literature. We use this connection to show that the identified set for structural parameters and functionals of the distribution of latent individual effects can be characterized by a finite set of conditional moment equalities subject to a certain set of shape constraints on the model parameters. In addition to providing a general approach to identification, the new characterization can deliver informative bounds in cases where competing methods deliver no identifying restrictions, and can deliver point identification in cases where competing methods deliver partial identification. We then present an estimation and inference procedure that uses semidefinite programming methods, is applicable with continuous or discrete covariates, and can be used for models that are either point- or partially-identified. Finally, we illustrate our identification result with a number of examples, and provide an empirical application to employment dynamics using data from the National Longitudinal Survey of Youth.
format Preprint
id arxiv_https___arxiv_org_abs_2104_04590
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Identification of Dynamic Panel Logit Models with Fixed Effects
Dobronyi, Christopher
Gu, Jiaying
Kim, Kyoo il
Russell, Thomas M.
Econometrics
Methodology
We show that identification in a general class of dynamic panel logit models with fixed effects is related to the truncated moment problem from the mathematics literature. We use this connection to show that the identified set for structural parameters and functionals of the distribution of latent individual effects can be characterized by a finite set of conditional moment equalities subject to a certain set of shape constraints on the model parameters. In addition to providing a general approach to identification, the new characterization can deliver informative bounds in cases where competing methods deliver no identifying restrictions, and can deliver point identification in cases where competing methods deliver partial identification. We then present an estimation and inference procedure that uses semidefinite programming methods, is applicable with continuous or discrete covariates, and can be used for models that are either point- or partially-identified. Finally, we illustrate our identification result with a number of examples, and provide an empirical application to employment dynamics using data from the National Longitudinal Survey of Youth.
title Identification of Dynamic Panel Logit Models with Fixed Effects
topic Econometrics
Methodology
url https://arxiv.org/abs/2104.04590