AOI approximately inverts the likelihood mapping from fixed effects to outcomes to produce an estimator whose bias vanishes exponentially in T with double robustness.
Identification of Dynamic Panel Logit Models with Fixed Effects
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
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.
representative citing papers
The paper proves that functionals of the mixing distribution in nonparametric empirical Bayes models can be consistently estimated via SPMLE even when the mixing distribution is unidentifiable due to nonresponse.
Derives sufficient statistics for feedback and heterogeneity in dynamic panel logit models, proves conditional likelihood identification is infeasible for Markov covariates, and proposes two assumptions to restore identification.
citing papers explorer
-
Approximate Operator Inversion for Average Effects in Nonlinear Panel Models
AOI approximately inverts the likelihood mapping from fixed effects to outcomes to produce an estimator whose bias vanishes exponentially in T with double robustness.
-
Empirical Bayes Estimation of the Mean of a Function of the Latent Variable with Applications to the Treatment of Nonresponse
The paper proves that functionals of the mixing distribution in nonparametric empirical Bayes models can be consistently estimated via SPMLE even when the mixing distribution is unidentifiable due to nonresponse.
-
Sufficient Statistics for Markovian Feedback Processes and Unobserved Heterogeneity in Dynamic Panel Logit Models
Derives sufficient statistics for feedback and heterogeneity in dynamic panel logit models, proves conditional likelihood identification is infeasible for Markov covariates, and proposes two assumptions to restore identification.