Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency

This paper analyzes the productivity of farms across 370 municipalities in the Center-West region of Brazil. A stochastic frontier model with a latent spatial structure is proposed to account for possible unknown geographical variation of the outputs. The paper compares versions of the model that include the latent spatial effect in the mean of output or as a variable that conditions the distribution of inefficiency, include or not observed municipal variables, and specify independent normal or conditional autoregressive priors for the spatial effects. The Bayesian paradigm is used to estimate the proposed models. As the resultant posterior distributions do not have a closed form, stochastic simulation techniques are used to obtain samples from them. Two model comparison criteria provide support for including the latent spatial effects, even after considering covariates at the municipal level. Models that ignore the latent spatial effects produce significantly different rankings of inefficiencies across agents.

Estimators and hypothesis tests for a stochastic frontier function: A Monte Carlo analysis

This paper uses Monte Carlo experimentation to investigate the finite sample properties of the maximum likelihood (ML) and corrected ordinary least squares (COLS) estimators of the half-normal stochastic frontier production function. Results indicate substantial bias in both ML and COLS when the percentage contribution of inefficiency in the composed error (denoted by *) is small, and also that ML should be used in preference to COLS because of large mean square error advantages when * is greater than 50%. The performance of a number of tests of the existence of technical inefficiency is also investigated. The Wald and likelihood ratio (LR) tests are shown to have incorrect size. A one-sided LR test and a test of the significance of the third moment of the OLS residuals are suggested as alternatives, and are shown to have correct size, with the one-sided LR test having the better power of the two.The author would like to thank Bill Griffiths, George Battese, Howard Doran, Bill Greene and two anonymous referees for valuable comments. Any errors which remain are those of the author.     

Bayesian stochastic frontier analysis using WinBUGS

Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This paper implements MCMC methods for Bayesian analysis of stochastic frontier models using the WinBUGS package, a freely available software. General code for cross-sectional and panel data are presented and various ways of summarizing posterior inference are discussed. Several examples illustrate that analyses with models of genuine practical interest can be performed straightforwardly and model changes are easily implemented. Although WinBUGS may not be that efficient for more complicated models, it does make Bayesian inference with stochastic frontier models easily accessible for applied researchers and its generic structure allows for a lot of flexibility in model specification.      

Analysis of electric distribution utilities efficiency levels by stochastic frontier in Brazilian power sector

The regulatory power sector model in several countries determines tariff review forms based, among other things, on sharing efficiency gains with consumers. As these reviews have an important impact on consumers and distribution utilities, it is necessary that the adopted methodologies always be improved. To this end, this article assessed a Bayesian inference application in order to estimate a stochastic cost frontier considering temporal efficiency dynamics. Taking this point into consideration is essential, since studies carried out to assess power sector efficiency have neglected the fact that part of efficiency increases originate from scale gain due to market expansion, which occurs over time. The sample assessed herein is composed of panel data from 61 electric power utilities between 2003 and 2016. The results demonstrate that the tariff review is positively affected by distributor efficiency.     

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility models.     

A new approach to estimating a profit frontier using the censored stochastic frontier model

The translog profit functional form is widely used to study technical efficiency for banks. Although this functional form is known as being flexible, it is not applicable to those banks incurring economic losses. The recently developed approach, i.e., the censored stochastic frontier model (CSFM), by Tsay et al. (2013) appears to be superior to existing approaches, since CSFM does not need to transform negative profit into positive profit before taking the natural logarithm. The transformation with respect to the profit variable tends to bias the parameter estimates of the profit frontier and the subsequent profit efficiency measure. We show that the parameter estimates of CSFM have the desirable statistical properties. Moreover, empirical results reveal that the mean profit efficiency of CSFM is more robust than those models using transformed profits across the sub-periods 1991–1998 and 1999–2009.     

On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data

We study the construction of confidence intervals for efficiency levels of individual firms in stochastic frontier models with panel data. The focus is on bootstrapping and related methods. We start with a survey of various versions of the bootstrap. We also propose a simple parametric alternative in which one acts as if the␣identity of the best firm is known. Monte Carlo simulations indicate that the parametric method works better than the␣percentile bootstrap, but not as well as bootstrap methods that make bias corrections. All of these methods are valid␣only for large time-series sample size (T), and correspondingly none of the methods yields very accurate confidence intervals except when T is large enough that the identity of the best firm is clear. We also present empirical results for two well-known data sets.      

Confidence statements for efficiency estimates from stochastic frontier models

This paper is an empirical study of the uncertainty associated with technical efficiency estimates from stochastic frontier models. We show how to construct confidence intervals for estimates of technical efficiency levels under different sets of assumptions ranging from the very strong to the relatively weak. We demonstrate empirically how the degree of uncertainty associated with these estimates relates to the strength of the assumptions made and to various features of the data.     

New evidence on the efficiency of Italian airports: A bootstrapped DEA analysis

A bootstrapped DEA procedure is used to estimate technical efficiency of 18 Italian airports during the period 2000-2004. Departing from previous studies, we separate the efficiency related to ability to manage airside activities (operational) from that related to the management of all business activities (financial). In general, Italian airports operate at poor levels of efficiency, with slightly better performance in terms of their financial activities. In the current study, selected intrinsic and environmental characteristics are considered as possible drivers of Italian airport performance. In particular, we found that: (i) the airport dimension does not allows for operational efficiency advantages, (ii) on the other hand, the airport dimension allows for financial efficiency advantages for the case of hubs and disadvantages for the case of the smallest airports (iii) the type(s) of concession agreement(s) might be considered as important source of technical efficiency differentials for those airports running marginal commercial activities; (iv) the introduction of a dual-till price cap regulation might create incentives which lead to the increase of financial efficiency at the detriment of the operational performance. Lastly, the development of a second hub (Milano Malpensa), has negatively affected the performance of the country’s national hub (Roma Fiumicino).     

Consistent estimation of the fixed effects stochastic frontier model

In this paper we consider a fixed-effects stochastic frontier model. That is, we have panel data, fixed individual (firm) effects, and the usual stochastic frontier analysis (SFA) composed error.     

Socio-institutional determinants of educational resource efficiency according to the capability approach: An endogenous stochastic frontier analysis

We investigate the role of private and public resources in educational attainments and the socio-institutional determinants of educational resource efficiency in Italy. Following the Sen's capability approach, we consider social capital and the quality of government part of the social conversion factors through which resources can be converted into human capabilities, such as education. We analyze the case of Italy by constructing a new longitudinal dataset from 1993 to 2012, using repeated cross sections from the main social survey of the Italian Statistical Institute and a panel stochastic frontier model that takes into account endogeneity. The results show the relative importance of private resources (measured by an ad hoc constructed wealth index), the complementarity between private wealth and public expenditures on education, and the positive impact of social capital and the quality of government on educational resource efficiency.     

Leverage effect on stochastic volatility for option pricing in Hong Kong: A simulation and empirical study

This paper explores the importance of incorporating the financial leverage effect in the stochastic volatility models when pricing options. For the illustrative purpose, we first conduct the simulation experiment by using the Markov Chain Monte Carlo (MCMC) sampling method. We then make an empirical analysis by applying the volatility models to the real return data of the Hang Seng index during the period from January 1, 2013 to December 31, 2017. Our results highlight the accuracy of the stochastic volatility models with leverage in option pricing when leverage is high. In addition, the leverage effect becomes more significant as the maturity of options increases. Moreover, leverage affects the pricing of in-the-money options more than that of at-the-money and out-of-money options. Our study is therefore useful for both asset pricing and portfolio investment in the Hong Kong market where volatility is an inherent nature of the economy.     

Money and production: A stochastic frontier approach

Considerable controversy surrounds the role of money in the production of goods and services. Previous empirical research has appeared to find that the real money stock affects aggregate output, holding other, more conventional inputs constant. However, the theoretical literature offers no convincing explanation for this empirical finding. One interpretation is that real money balances reduce the extent to which labor and capital are diverted into exchange-related activities instead of being used in production defined in a more narrow sense. To investigate this hypothesis, we estimate a production function augmented with real money balances as an input, using time-series data for the aggregate U.S. economy. A stochastic production frontier is then estimated without real money balances. We use these estimates to establish the presence of technical inefficiency. Finally, we show that the extent of technical inefficiency is negatively correlated with the real money stock. Our results provide a reconciliation between the empirical literature, which finds that real money balances affect output in a production function framework, and the theoretical literature, which suggests that real money balances enhance the technical efficiency of the economy.     

Matching in local labor markets: a stochastic frontier approach

This paper investigates the technical efficiency of labor market matching from a stochastic frontier approach. The true fixed-effects model (Greene J Prod Anal 23:7–32, 2005a; J Econom 126:269–303, 2005b) is utilised in order to separate cross-sectional heterogeneity from inefficiency, and inefficiency terms are modelled following Battese and Coelli (Empir Econ 20:325–332, 1995). The data set consists of almost 17,000 observations from Local Labor Offices (LLOs) in Finland. According to the results, there are notable differences in matching efficiency between regions, and these differences contribute significantly to the number of filled vacancies. If all regions were as efficient as the most efficient one, the number of total matches per month would increase by over 23%. The heterogeneity of the job-seeker stock is an important determinant of matching efficiency: the weight of the composition of the job-seeker stock in the inefficiency terms is on average 85%.

Model selection in stochastic frontier analysis with an application to maize production in Kenya

This paper shows how to compute the standard errors for partial effects of exogenous firm characteristics influencing firm inefficiency under a range of popular stochastic frontier model specifications. We also develop an R²-type measure to summarize the overall explanatory power of the exogenous factors on firm inefficiency. The paper also applies a recently developed model selection procedure to choose among alternative stochastic frontier specifications using data from household maize production in Kenya. The magnitude of estimated partial effects of exogenous household characteristics on inefficiency turns out to be very sensitive to model specification, and the model selection procedure leads to an unambiguous choice of best model. We propose a bootstrapping procedure to evaluate the size and power of the model selection procedure. The empirical application also provides further evidence on how household characteristics influence technical inefficiency in maize production in developing countries.

A threshold stochastic volatility model with explanatory variables

In this paper, we introduce a threshold stochastic volatility model with explanatory variables. The Bayesian method is considered in estimating the parameters of the proposed model via the Markov chain Monte Carlo (MCMC) algorithm. Gibbs sampling and Metropolis–Hastings sampling methods are used for drawing the posterior samples of the parameters and the latent variables. In the simulation study, the accuracy of the MCMC algorithm, the sensitivity of the algorithm for model assumptions, and the robustness of the posterior distribution under different priors are considered. Simulation results indicate that our MCMC algorithm converges fast and that the posterior distribution is robust under different priors and model assumptions. A real data example was analyzed to explain the asymmetric behavior of stock markets.     

Water use efficiency and public goods conservation: A spatial stochastic frontier model applied to irrigation in Southern Italy

Water used for irrigation is essential for global food production. Increased water scarcity, due to climate change, is a constraint to agricultural development, especially in arid and semi-arid areas. This increases pressure on agriculture which often manages water inefficiently and competes with other sectors for water use. Enhancing farmers’ production efficiency may lead to substantial water savings and conservation. Public sector is called to play a role in water governance and to introduce appropriate multilevel regulatory and incentive measures for better water management. This work applies a spatial stochastic frontier model to the case of high water-demanding fruit and vegetable crops in the Apulia region of Southern Italy, where water is scarce due to semi-arid climate and erratic rainfall. Using cross-sectional data from the EU Farm Accountancy Data Network, this work incorporates firm specific heterogeneity into technical efficiency analysis and implements an autoregressive specification of the inefficiency component. Results support the hypothesis that spatial heterogeneity exists in on-farm efficiency of irrigated crop production and is adequately captured by the spatial stochastic frontier model approach. Technical efficiency of farms with similar structural and management characteristics greatly varies across crops and geographical areas, because of the different natural resource endowment and agro-climatic factors. Policies providing incentives to on-farm adoption of modern water-saving technologies and measures to promote small family farm activities could effectively contribute to water conservation goal, but they should be well-articulated to account for agriculture spatial diverseness.     

Generalized smooth finite mixtures

We propose a general class of models and a unified Bayesian inference methodology for flexibly estimating the density of a response variable conditional on a possibly high-dimensional set of covariates. Our model is a finite mixture of component models with covariate-dependent mixing weights. The component densities can belong to any parametric family, with each model parameter being a deterministic function of covariates through a link function. Our MCMC methodology allows for Bayesian variable selection among the covariates in the mixture components and in the mixing weights. The model’s parameterization and variable selection prior are chosen to prevent overfitting. We use simulated and real data sets to illustrate the methodology.     

Bayesian modification indices for IRT models

Bayesian modification indices are presented that provide information for the process of model evaluation and model modification. These indices can be used to investigate the improvement in a model if fixed parameters are re-specified as free parameters. The indices can be seen as a Bayesian analogue to the modification indices commonly used in a frequentist framework. The aim is to provide diagnostic information for multi-parameter models where the number of possible model violations and the related number of alternative models is too large to render estimation of each alternative practical. As an example, the method is applied to an item response theory (IRT) model, that is, to the two-parameter model. The method is used to investigate differential item functioning and violations of the assumption of local independence.     

Time varying VARs with inequality restrictions

In many applications involving time-varying parameter VARs, it is desirable to restrict the VAR coefficients at each point in time to be non-explosive. This is an example of a problem where inequality restrictions are imposed on states in a state space model. In this paper, we describe how existing MCMC algorithms for imposing such inequality restrictions can work poorly (or not at all) and suggest alternative algorithms which exhibit better performance. Furthermore, we show that previous algorithms involve an approximation relating to a key prior integrating constant. Our algorithms are exact, not involving this approximation. In an application involving a commonly used U.S. data set, we present evidence that the algorithms proposed in this paper work well.