Revisiting Panel Data Binary Choice Models with Lagged Dependent Variables

Abstract This article revisits the identification and estimation of a class of semiparametric (distribution-free) panel data binary choice models with lagged dependent variables, exogenous covariates, and entity fixed effects. We provide a novel identification strategy, using an “identification at infinity” argument. In contrast with the celebrated work by Honoré and Kyriazidou published in 2000, our method permits time trends of any form and does not suffer from the “curse of dimensionality”. We propose an easily implementable conditional maximum score estimator. The asymptotic properties of the proposed estimator are fully characterized. A small-scale Monte Carlo study demonstrates that our approach performs satisfactorily in finite samples. We illustrate the usefulness of our method by presenting an empirical application to enrollment in private hospital insurance using the Household, Income and Labor Dynamics in Australia (HILDA) Survey data.

• Publication year

  2024

• Venue

  Journal of Business & Economic Statistics

• Publication date

  2024-10-15

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1080/07350015.2024.2412006
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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