Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper develops nonparametric identification and estimation results for a single-spell hazard model, where the unobserved heterogeneity is specified as a Lévy subordinator. The identification approach solves a nonlinear Volterra integral equation of the first kind with an unknown kernel function. Both the kernel of the integral operator, which models the distribution of the unobserved heterogeneity, and the functions that enter it are identified given regularity conditions and minimal variation in the observed covariates. The paper proposes a shape-constrained nonparametric two-step sieve minimum distance estimator. Rates of convergence are derived and Monte Carlo experiments show the finite sample performance of the estimator.