Semiparametric identification and heterogeneity in discrete choice dynamic programming models

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An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling (2005, result): “This model is similar to other types of selection models (see, e.g., Heckman, 1990, Cameron and Heckman, 1998, or Taber, 2000), so we appeal to the results in that literature.”
Venture capital investments and the technological performance of portfolio firms (2016, unknown): “Identification in semiparametric models of binary choice variables often relies on exclusion restrictions (Heckman, 1990; Taber, 2000)—in our parametric case, however, the functional form is sufficient for identification.”
An Empirical Investigation of the Option Value of College Enrollment (2012, methodology): “…two periods, which correspond to the first and second half of college.11 Utility is in dollars, individuals are assumed to be 11A similar two-period setup was used by Manski (1989); Altonji (1993); Christopher R. Taber (2000); and Arcidia- cono (2004). risk-neutral, and time discounting is ignored.”
Identifying Dynamic Discrete Decision Processes (2002, background): “…equations as unobserved types, equation (2) can be rewritten as The difficulty with respect to the homogenous case arises from the presence of dynamic selection or endogeneity biases (Cameron and Heckman (1998), Taber (2000)) because one state-variable, 0, remains unobserved by the econometrician.”