The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.

In this paper, we present an econometric analysis of the effects of a price floor on price dynamics and price volatility. A price floor (implemented as a part of government pricing policy) provides a censoring mechanism for price determination. We specify and estimate a dynamic Tobit model under time-varying volatility. The model is applied to analyze the effects of a price support program on price dynamics and price volatility in the U.S. American cheese market. The econometric analysis provides useful insights on price dynamics in the presence of a government-determined price floor.