Identification of Marginal Effects in a Nonparametric Correlated Random Effects Model

In this paper, we consider identification and estimation of average marginal effects in a correlated random effects model without imposing strong functional form assumptions on the structural likelihood or the mixing distribution. Identification is achieved through imposing that the mixing distribution depends on observed covariates only through an index function and the assumption that at least three time periods are available for each cross sectional unit. We leave the functional form of the index function unrestricted subject to smoothness conditions. We present identification results for this model and consider estimation of the marginal effects of interest. We illustrate the use of the approach through a brief empirical example which considers the relationship between insider trading activity and trading volume.