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Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This paper aims to explore the use of the data envelopment analysis (DEA), Cobb-Douglas and translog production function methods in estimating contractors’ efficiency.

In this paper, the DEA, translog and Cobb-Douglas methods were used to estimate the technical efficiency of 23 contractors in Hong Kong from 2003 to 2009. For this research, four input and three output variables were identified.

The results suggest that the efficiency scores obtained from the DEA method were significantly different from those obtained from the translog and Cobb-Douglas methods, while the efficiency scores from the translog method were similar to those from the Cobb-Douglas method. The DEA method further reveals that the company had poor utilisation of its resources over the past few years. On the output side, the current ratio was too small, implying that the company suffered from excess current liabilities relative to its current assets.

Application of efficiency measurement in the built environment is still in its infancy. The current research, therefore, calls for more research to be undertaken to establish the applied literature base for the construction industry.

The DEA method helps the inefficient company explore ways to improve the utilisation of the inputs as well as the process and to maximise the outputs.

Knowing the relative performance of contractors helps understand their competitiveness in the construction industry. By estimating their technical efficiency, contractors can improve the conditions for enhancing performance.

We consider a semiparametric panel stochastic frontier model where one-sided firm effects representing inefficiencies are correlated with the regressors. A form of the Chamberlain-Mundlak device is used to relate the logarithm of the effects to the regressors resulting in a lognormal distribution for the effects. The function describing the technology is modeled nonparametrically using penalized splines. Both Bayesian and non-Bayesian approaches to estimation are considered, with an emphasis on Bayesian estimation. A Monte Carlo experiment is used to investigate the consequences of ignoring correlation between the effects and the regressors, and choosing the wrong functional form for the technology.

This paper studies the profit efficiency of a sample of large U.S. commercial banks and explores how this performance varies with selected measures of bank risk reflecting aspects of credit risk, liquidity risk, and insolvency risk. We use a standard profit function and the stochastic frontier approach, and compare two standard functional forms – Cobb‐Douglas and translog – to assess the tradeoff between precision and parsimony. We find that profit efficiency is sensitive to credit risk and insolvency risk but not to liquidity risk or to the mix of loan products.

Since there is no agreement on the consistency of their estimates, the purpose of this paper is to investigate whether parametric stochastic frontier analysis (SFA) and nonparametric data envelopment analysis (DEA) generate consistent bank efficiency assessments.

The authors utilize four alternative efficiency computation models: two DEA technical efficiency models based on constant and variable returns to scale, and two SFA cost efficiency models employing Translog and Fourier functional specifications. An unbalanced panel of 59 Indian banks over 1990‐2007 is employed as a model, developing country, banking market.

The Translog and Fourier specifications in SFA and the constant and variable returns to scale assumptions in DEA are found to rank and identify “best‐practice” and “worst‐practice” approximately in the same order. The association between DEA efficiency estimates and non‐frontier standard performance measures, however, is mixed and inconclusive. Unlike DEA scores, SFA efficiency assessments were found to be consistent with cost and profit ratios and hence are “believable”.

For regulators and bankers alike, the authors' findings highlight the importance of investigating the consistency of efficiency scores across various research methods. They should ensure that frontier‐based efficiency assessments are not simply “artificial constructs” of models' assumptions/specifications.

This paper extends the existing literature by checking jointly the statistical consistency of both DEA technical efficiency scores and SFA cost efficiency scores. The prior studies focus either on technical efficiency or cost efficiency, but not both. Moreover, as far as the authors are aware, this is the first cross‐methodological validation study to focus on bank efficiency in the context of a developing country banking market.

This chapter reviews the specification and application of the Deaton and Muellbauer's (1980) almost ideal demand system (AIDS) and the Christensen et al. (1975) translog (TL) demand system. In so doing we examine various refinements to these models, including ways of incorporating demographic effects, methods by which curvature conditions can be imposed, and issues associated with incorporating structural change and seasonal effects. We also review methods for adjusting for autocorrelation in the models' residuals. A set of empirical examples for the AIDS and the log TL version of the translog based on historical meat price and consumption data for the United States are also presented.