Fixed and Random Effects in Stochastic Frontier Models

Stochastic Nonparametric Envelopment of Panel Data: Frontier Estimation with Fixed and Random Effects Approaches

Stochastic nonparametric envelopment of data (StoNED) combines the virtues of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) into a unified framework of frontier estimation. StoNED melds the nonparametric piece-wise linear DEA-type frontier with stochastic SFA-type inefficiency and noise terms. We show that the StoNED model can be estimated in the panel data setting in a...

Fixed and Random Effects in Nonlinear Models

This paper surveys recently developed approaches to analyzing panel data with nonlinear models. We summarize a number of results on estimation of fixed and random effects models in nonlinear modeling frameworks such as discrete choice, count data, duration, censored data, sample selection, stochastic frontier and, generally, models that are nonlinear both in parameters and variables. We show th...

Consistent Estimation of the Fixed Effects Stochastic Frontier Model

Stochastic Frontier Models

Estimation of Stochastic Frontier Models with Fixed Effects through Monte Carlo Maximum Likelihood

Estimation of nonlinear fixed-effects models is plagued by the incidental parameters problem. This paper proposes a procedure for choosing appropriate densities for integrating the incidental parameters from the likelihood function in a general context. The densities are based on priors that are updated using information from the data and are robust to possible correlation of the group-specific...