Nonseparable panel models with index structure and correlated random effects

Pavel Cizek*, Serhan Sadikoglu

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

To facilitate semiparametric estimation of general discrete-choice, censored, sample selection, and other complex panel data models, we study identification and estimation of nonseparable multiple-index models in panel data with correlated random effects and a fixed number of time periods. The parameter vectors of interest are shown to be identified up to multiplicative constants and the average marginal effects are identified under the assumption that the distribution of individual effects depends on the explanatory variables only through their averages across time. Under this assumption, we propose to estimate the unknown parameters by the generalized method of moments based on the average and outer product of the difference of derivatives of the regression function. The rate of convergence and asymptotic distribution is established both for the proposed parameter estimates and the average marginal effects. We conduct Monte Carlo simulation studies to assess finite-sample performance of the proposed estimator. We also use it to model dynamic earnings of women, and using our general identification results, we find a negative effect of the number of children not only on the selection into employment as usual, but also on the average earnings.
Original languageEnglish
Pages (from-to)246-274
Number of pages29
JournalEconometric Reviews
Volume44
Issue number3
DOIs
Publication statusPublished - 16 Mar 2025

Keywords

  • correlated random effects
  • local polynomia smoothing
  • multiple-index model
  • nonlinear panel data
  • outer product of gradients

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