This article analyzes the identifiability of k-variate, M-component finite mixture models in which each component distribution has independent marginals, including models in latent class analysis. Without making parametric assumptions on the component distributions, we investigate how one can...

In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component...

Commonly used methods of production function estimation assume that a firm's output quantity can be observed as data, but typical datasets contain only revenue, not output quantity. We examine the nonparametric identification of production function from revenue data when a firm faces a general...

Commonly used methods of production function and markup estimation assume that a firm’s output quantity can be observed as data, but typical datasets contain only revenue, not output quantity. We examine the nonparametric identification of production function and markup from revenue data when...

Commonly used methods of production function estimation assume that a firm’s output quantity can be observed as data, but typical datasets contain only revenue, not output quantity. We examine the nonparametric identification of production function from revenue data when a firm faces a general...