Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions

Researchers employ the directional distance function (DDF) to estimate multiple-input and multiple-output production, firm inefficiency, and productivity growth. We relax restrictive assumptions by computing optimal directions subject to profit maximization and cost minimization, correct for the potential endogeneity of inputs and outputs, estimate latent prices for bad outputs, measure firms’ responses to shadow prices rather than actual prices, and introduce an unobserved productivity term into the DDF. For an unbalanced panel of U.S. electric utilities, a model assuming profit-maximization outperforms one assuming cost-minimization, while lagged productivity and energy price have the greatest effect on productivity

Measuring productivity and efficiency: a Kalman filter approach

In the Kalman filter setting, one can model the inefficiency term of the standard stochastic frontier composed error as an unobserved state. In this study a panel data version of the local level model is used for estimating time-varying efficiencies of firms. We apply the Kalman filter to estimate average efficiencies of U.S. airlines and find that the technical efficiency of these carriers did not improve during the period 1999-2009. During this period the industry incurred substantial losses, and the efficiency gains from reorganized networks, code-sharing arrangements, and other best business practices apparently had already been realized

Foreign presence, technical efficiency and firm survival in Greece: a simultaneous equation model with latent variables approach

The aim of the paper is to explain the role that technical efficiency and foreign spillover effects have on firm survival. Panel data from Greek manufacturing industry (3142 firms) in 1997-2003 are used. Technical efficiency is estimated through a CES translog production function. A hazard function is then used (corresponding to the Exponential and Weibull distributions as well as the Cox model) to estimate survival probabilities. While foreign spillovers exercise a positive impact on hazard, foreign firms do not have any distinctive survival advantage compared to their domestic rivals. On the contrary, technical efficiency affects hazard in a negative way, improving survival expectations

Modelling the covariance structure in marginal multivariate count models: Hunting in Bioko Island.

The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson–Tweedie distribution. Furthermore, the score information criterion is extended for selecting the components of the matrix linear predictor. We analyse a data set consisting of prey animals (the main hunted species, the blue duiker Philantomba monticola and other taxa) shot or snared for bushmeat by 52 commercial hunters over a 33-month period in Pico Basilé, Bioko Island, Equatorial Guinea. By taking into account the severely unbalanced repeated measures and longitudinal structures induced by the hunters and a set of potential covariates (which in turn affect the mean and covariance structures), our method can be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited areas anywhere in the world. We suggest that our method can be used to more accurately understand the trajectories of animals hunted for commercial or subsistence purposes and establish clear policies to ensure sustainable hunting practices

Likelihood Analysis of Random Effect Stochastic Frontier Models with Panel Data

The paper takes up posterior analysis of the stochastic frontier model with random effects when panel data is available. Available treatments of the model result in a likelihood function that is highly nonlinear and, as a result, applied researchers prefer to use fixed effect formulations when efficiency measurement is sought from panel data. The methodology is based on Gibbs sampling. It is shown how posterior distributions of parameters can be derived and how firm-specific efficiency measures can be computed.