Curvature-Constrained Estimates of Technical Efficiency and Returns to Scale for U.S. Electric Utilities

We estimate an input distance function for U.S. electric utilities under the assumption that non-negative variables associated with technical inefficiency are timeinvariant. We use Bayesian methodology to impose curvature restrictions implied by microeconomic theory and obtain exact finite-sample results for nonlinear functions of the parameters (eg. technical efficiency scores). We find that Bayesian point estimates of elasticities are more plausible than maximum likelihood estimates, technical efficiency scores from a random effects specification are higher than those obtained from a fixed effects model, and there is evidence of increasing returns to scale in the industry.

ESTIMATING STATE-CONTINGENT PRODUCTION FRONTIERS

Chambers and Quiggin (2000) advocate the use of state-contingent production technologies to represent risky production and establish important theoretical results concerning producer behaviour under uncertainty. Unfortunately, perceived problems in the estimation of state-contingent models have limited the usefulness of the approach in policy formulation. We show that fixed and random effects state-contingent production frontiers can be conveniently estimated in a finite mixtures framework. An empirical example is provided. Compared to standard estimation approaches, we find that estimating production frontiers in a state-contingent framework produces significantly different estimates of elasticities, firm technical efficiencies and other quantities of economic interest.

A Bayesian Approach To Imposing Curvature On Distance Functions

The estimated parameters of output distance functions frequently violate the monotonicity, quasiconvexity and convexity constraints implied by economic theory, leading to estimated elasticities and shadow prices that are incorrectly signed, and ultimately to perverse conclusions concerning the effects of input and output changes on productivity growth and relative efficiency levels. We show how a Bayesian approach can be used to impose these constraints on the parameters of a translog output distance function. Implementing the approach involves the use of a Gibbs sampler with data augmentation. A Metropolis-Hastings algorithm is also used within the Gibbs to simulate observations from truncated pdfs. Our methods are developed for the case where panel data is available and technical inefficiency effects are assumed to be time-invariant. Two models � a fixed effects model and a random effects model � are developed and applied to panel data on 17 European railways. We observe significant changes in estimated elasticities and shadow price ratios when regularity restrictions are imposed

Production Under Uncertainty: A Simulation Study

In this article we model production technology in a state-contingent framework. Our model analyzes production under uncertainty without being explicit about the nature of producer risk preferences. In our model producers’ risk preferences are captured by the risk-neutral probabilities they assign to the different states of nature. Using a state-general state-contingent specification of technology we show that rational producers who encounter the same stochastic technology can make significantly different production choices. Further, we develop an econometric methodology to estimate the risk-neutral probabilities and the parameters of stochastic technology when there are two states of nature and only one of which is observed. Finally, we simulate data based on our state-general state-contingent specification of technology. Biased estimates of the technology parameters are obtained when we apply conventional ordinary least squares (OLS) estimator on the simulated data.CES, Cobb-Douglas, OLS, output-cubical, risk-neutral, state-allocable, state-contingent

Uncertainty and technical efficiency in Finnish agriculture: a state-contingent approach

In this article, we present one of the first real-world empirical applications of state-contingent production theory. Our state-contingent behavioral model allows us to analyze production under both inefficiency and uncertainty without regard to the nature of producer risk preferences. Using farm data for Finland, we estimate a flexible production model that permits substitutability between state-contingent outputs. We test empirically, and reject, an assumption that has been implicit in almost all efficiency studies conducted in the last three decades, namely that the production technology is output-cubical, i.e., that outputs are not substitutable between states of nature.state-contingent; production; uncertainty

Imposing Observation-Varying Equality Constraints Using Generalised Restricted Least Squares

Linear equality restrictions derived from economic theory are frequently observation-varying. Except in special cases, Restricted Least Squares (RLS) cannot be used to impose such restrictions without either underconstraining or overconstraining the parameter space. We solve the problem by developing a new estimator that collapses to RLS in cases where the restrictions are observation-invariant. We derive some theoretical properties of our so-called Generalised Restricted Least Squares (GRLS) estimator, and conduct a simulation experiment involving the estimation of a constant returns to scale production function. We find that GRLS significantly outperforms RLS in both small and large samples.

Grammar induction for mildly context sensitive languages using variational Bayesian inference

The following technical report presents a formal approach to probabilistic minimalist grammar induction. We describe a formalization of a minimalist grammar. Based on this grammar, we define a generative model for minimalist derivations. We then present a generalized algorithm for the application of variational Bayesian inference to lexicalized mildly context sensitive language grammars which in this paper is applied to the previously defined minimalist grammar

A Bayesian Approach To Imposing Curvature On Distance Functions

The estimated parameters of output distance functions frequently violate the monotonicity, quasiconvexity and convexity constraints implied by economic theory, leading to estimated elasticities and shadow prices that are incorrectly signed, and ultimately to perverse conclusions concerning the effects of input and output changes on productivity growth and relative efficiency levels. We show how a Bayesian approach can be used to impose these constraints on the parameters of a translog output distance function. Implementing the approach involves the use of a Gibbs sampler with data augmentation. A Metropolis-Hastings algorithm is also used within the Gibbs to simulate observations from truncated pdfs. Our methods are developed for the case where panel data is available and technical inefficiency effects are assumed to be time-invariant. Two models - a fixed effects model and a random effects model - are developed and applied to panel data on 17 European railways. We observe significant changes in estimated elasticities and shadow price ratios when regularity restrictions are imposed