Sensitivity analysis of efficiency rankings to distributional assumptions: applications to Japanese water utilities

This paper examines the robustness of efficiency score rankings across four distributional assumptions for trans-log stochastic production-frontier models, using data from 1,221 Japanese water utilities (for 2004 and 2005). One-sided error terms considered include the half-normal, truncated normal, exponential, and gamma distributions. Results are compared for homoscedastic and doubly heteroscedastic models, where we also introduce a doubly heteroscedastic variable mean model, and examine the sensitivity of the nested models to a stronger heteroscedasticity correction for the one-sided error component. The results support three conclusions regarding the sensitivity of efficiency rankings to distributional assumptions. When four standard distributional assumptions are applied to a homoscedastic stochastic frontier model, the efficiency rankings are quite consistent. When those assumptions are applied to a doubly heteroscedastic stochastic frontier model, the efficiency rankings are consistent when proper and sufficient arguments for the variance functions are included in the model. When a more general model, like a variable mean model is estimated, efficiency rankings are quite sensitive to heteroscedasticity correction schemes.stochastic production frontier models; Japanese water utilities; heteroscedasticity

Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression

The standard Gaussian Process regression (GP) is usually formulated under stationary hypotheses: The noise power is considered constant throughout the input space and the covariance of the prior distribution is typically modeled as depending only on the difference between input samples. These assumptions can be too restrictive and unrealistic for many real-world problems. Although nonstationarity can be achieved using specific covariance functions, they require a prior knowledge of the kind of nonstationarity, not available for most applications. In this paper we propose to use the Laplace approximation to make inference in a divisive GP model to perform nonstationary regression, including heteroscedastic noise cases. The log-concavity of the likelihood ensures a unimodal posterior and makes that the Laplace approximation converges to a unique maximum. The characteristics of the likelihood also allow to obtain accurate posterior approximations when compared to the Expectation Propagation (EP) approximations and the asymptotically exact posterior provided by a Markov Chain Monte Carlo implementation with Elliptical Slice Sampling (ESS), but at a reduced computational load with respect to both, EP and ESS

Semiparametric Estimation of Heteroscedastic Binary Sample Selection Model

Binary choice sample selection models are widely used in applied economics with large cross-sectional data where heteroscedaticity is typically a serious concern. Existing parametric and semiparametric estimators for the binary selection equation and the outcome equation in such models suffer from serious drawbacks in the presence of heteroscedasticity of unknown form in the latent errors. In this paper we propose some new estimators to overcome these drawbacks under a symmetry condition, robust to both nonnormality and general heterscedasticity. The estimators are shown to be $\sqrt{n}$-consistent and asymptotically normal. We also indicate that our approaches may be extended to other important models.

LOGIT MODELS FOR POOLED CONTINGENT VALUATION AND CONTINGENT RATING AND RANKING DATA: VALUING BENEFITS FROM FOREST BIODIVERSITY CONSERVATION

Contingent valuation and contingent rating and ranking methods for measuring willingness-to-pay for non-market goods are compared by using random coefficient models and data pooling methods. Pooled models on CV data and CR data on the preferred choice accept pooling if scale differences between the model estimates of CV and CR methods are allowed for. More detailed response models, such as pooled CV model and rank-ordered models for two or three ranks, reject pooling of the data.Resource /Energy Economics and Policy,