Unobserved heterogeneity in stochastic cost frontier models: a comparative analysis

This paper embeds Terrell's (1996) method of incorporating monotonicity and concavity conditions into the Bayesian stochastic frontier model proposed by Koop, Osiewalski, and Steel (1994). This method ensures analysis based on a cost function consistent with economic theory, guarantees

Abstract not found

In this paper the relative cost efficiency of Scottish farms is determined, and variables that explain this efficiency by farm type are identified and implications discussed. A panel dataset from the Farm Accounts Scheme (FAS) survey for the period 1997-2004 was used for the estimation. A cost

A random stochastic cost frontier model is specified for a sample of Mexican banks operating between 1998 and 2006. Our results show that the random frontier accounts for heterogeneity between banks, which can be explained by variation in bank outputs. The non-random frontier performs poorly

In this study we analyze the evolution of operating cost inefficiency for the English and Welsh water industry over the period 1995-2001 by estimating an heteroskedastic stochastic variable cost frontier. The main aim of this paper is to provide an overall picture of the industry cost inefficiency

Since the mid 1990s, with the benefit of a wider understanding of the potential benefits from yardstick competition between regional monopolies (Schleifer, 1985), practioners and academics specialising in regulatory issues have been increasingly interested in developing standardised performance indicators for monopolies in the infrastructure sector. These indicators can be used by the regulators to

this copyright notice appears on all such copies. This project was funded by the NMFS grant

Abstract: A stochastic frontier production function is defined for panel data on firms, in which the non-negative technical inetGciency effects are assumed to be a function of firm-specific variables and time. The inefficiency effects are assumed to be independently distributed as truncations

are represented by sparse graphs, whose vertices are labeled by a multi-resolution description in terms of a local power spectrum, and whose edges are labeled by geometrical distance vectors. Object recognition can be formulated as elastic graph matching, which is performed here by stochastic optimization of a