In this paper we propose new estimation techniques in connection with regression models whose errors have distributions which are members of the celebrated Pearson’s system. Efficient MCMC procedures are proposed in the context of likelihood—based inference. The new techniques are applied to...

A convenient and simple GMM procedure for estimating stochastic frontier models in the presence of endogenous regressors is proposed. Monte Carlo simulations show that the proposed estimator works very well in finite samples. We apply the proposed method to panel data of Norwegian dairy farms to...

This paper provides a link between the structural approach of econometrics of auctions with the stochastic frontier literature in an effort to model excess bidding behaviour. The new technique is applied to timber auction data. The results suggest that both systematic overbidding and...

In efficiency studies using the stochastic frontier approach, the main focus is to explain inefficiency in terms of some exogenous variables and computation of marginal effects of each of these determinants. Although inefficiency is estimated by its mean conditional on the composed error term...

This paper considers a generalized panel data model of polychotomous and/or sequential switching which can also accommodate the dependence between unobserved effects and covariates in the model. We showcase our model using an empirical illustration in which we estimate scope economies for the...