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In recent years, some interested data can be recorded only if the values fall within an interval range, and the responses are often subject to censoring. Attempting to perform effective statistical analysis with censored, especially heavy-tailed and asymmetric data, can be difficult. In this paper, we develop a novel linear regression model based on the proposed skewed generalized t distribution for censored data. The likelihood-based inference and diagnostic analysis are established using the Expectation/Conditional Maximization Either algorithm in conjunction with smoothing approximate functions. We derive relevant measures to perform global influence for this novel model and develop local influence analysis based on the conditional expectation of the complete-data log-likelihood function. Some useful perturbation schemes are discussed. We illustrate the finite sample performance and the robustness of the proposed method by simulation studies. The proposed model is compared with other procedures based on a real dataset, and a sensitivity analysis is also conducted. [ABSTRACT FROM AUTHOR]

Healthcare spending is a critical problem for any state that seeks to ensure that its citizens have equal access to essential health services. How do socioeconomic and demographic factors affect health spending in Turkey? The motivation for the study is to find an answer to this question. The aim of the study is to evaluate the determinants of healthcare spending of households, which constitute one of the most essential unions that make up society within a socioeconomic and demographic framework. In this regard, the Tobit model was utilized in the study using official institution data. As a result of the study, it has been determined that socioeconomic and demographic factors have an impact on healthcare spending. It is expected that the results of the study will be beneficial to policy makers, decision makers and those who make research about the factors that will ensure equality in health. [ABSTRACT FROM AUTHOR]

The high level of tobacco use in Turkey and across the world, causes a high number of health problems. This study explores the indicators of tobacco expenditure of households, the smallest unit of a society. In this context, the sociodemographic factors affecting household tobacco use were examined, and recommendations are made to reduce tobacco product impact in terms of users and those who are exposed to smoke. This study uses the dataset acquired from the Household Budget Survey. The factors affecting household expenditure on tobacco products were evaluated using the Tobit model. Gender, age, educational status, marital status, income level, and social variables were found to be effective indicators of tobacco product expenditure. The study aims to bring the following issues to the attention of decision-makers and policymakers: Priority should be given to tobacco control programs, particularly for young, single, male, and lowly educated individuals; raise tobacco taxes in a way that does not adversely affect the financial situation of low-income smokers who use such products; and encourage the implementation of evidence-based smoking cessation methods. [ABSTRACT FROM AUTHOR]

In the censored regression model, the Tobit maximum likelihood estimator is unstable and inefficient in the occurrence of the multicollinearity problem. To reduce this problem's effects, the Tobit ridge and the Tobit Liu estimators are proposed. Therefore, this study proposes a new kind of the Tobit estimation called the Tobit new ridge-type (TNRT) estimator. Also, the TNRT estimator was theoretically compared with the Tobit maximum likelihood, the Tobit ridge, and the Tobit Liu estimators via the mean squared error criterion. Moreover, we performed a Monte Carlo simulation to study the performance of the TNRT estimator compared with the previously defined estimators. Also, we used the Mroz dataset to confirm the theoretical and the simulation study results.

Abstract In this paper, we propose the power Student-t regression model for censored (limited) observations which extends the Student-t censored regression model. This extension is based on the asymmetric and heavy-tailed power Student-t distribution. The score functions and expected information matrix are given as well as the process for estimating the parameters in the model is discussed by using the likelihood approach. Two simulation studies are conducted to evaluate parameter recovery and properties of the model and finally, two applications to a real data set are reported to demonstrate the usefulness of this new methodology.

It is well-known that classical Tobit estimator of the parameters of the censored regression (CR) model is inefficient in case of non-normal error terms. In this paper, we propose to use the modified maximum likelihood (MML) estimator under the Jones and Faddy's skew t-error distribution, which covers a wide range of skew and symmetric distributions, for the CR model. The MML estimators, providing an alternative to the Tobit estimator, are explicitly expressed and they are asymptotically equivalent to the maximum likelihood estimator. A simulation study is conducted to compare the efficiencies of the MML estimators with the classical estimators such as the ordinary least squares, Tobit, censored least absolute deviations and symmetrically trimmed least squares estimators. The results of the simulation study show that the MML estimators work well among the others with respect to the root mean square error criterion for the CR model. A real life example is also provided to show the suitability of the MML methodology. [ABSTRACT FROM AUTHOR]

Recent sports economics literature has focused on 'superstar effects,' or the independent impact of certain exceptional players on fan attendance at live events. This paper investigates the effect of top-drafted rookies on game attendance in the National Basketball Association from the 2012–13 through 2018–19 seasons. Hypothesizing that the nature of modern basketball could lend unique popularity to top draft picks during their rookie seasons, I find that the presence of top-ten draftees on both home and visiting teams is associated with average increases between 150 and 190 spectators per game throughout the second half of their rookie seasons. These marginal effects are statistically indistinguishable from the impact of an additional All-Star player on the visiting team. Unlike previous work, the censored regression model used in this paper accounts for teams' use of standing room to generate attendance beyond their listed arena capacities, a relevant consideration in over a quarter of the 8,594-game sample. [ABSTRACT FROM AUTHOR]

Multicollinearity is often ignored in censored regression model. In this respect, Tobit Liu estimation is proposed as an alternative in order to dispel the adverse effects of multicollinearity on the maximum likelihood estimation. Tobit Liu estimator is compared with Tobit ridge estimator and Tobit maximum likelihood estimator, theoretically via mean square error. For empirical analysis, we prefer Mroz dataset which is a phenomenon in the context of censored regression. Since many studies have investigated Mroz dataset by neglecting multicollinearity, we handle this dataset in the context of multicollinearity in this paper. Moreover, it is seen that multicollinearity is not included in pioneer studies' simulation designs. Therefore, we conduct a Monte Carlo simulation study where we take account of different levels of multicollinearity while generating data. Briefly, this study contributes to filling an important gap in the literature and proposes to investigate other econometrically censored data in the context of multicollinearity. [ABSTRACT FROM AUTHOR]

There is a cost of educational attainment, which is covered by households as well as the state. While government spending on education emphasizes public responsibility for the realization and support of the existing service, education expenditures by households emphasize benefiting from educational services. The expenditures made by households differ by some socio-economic factors and are not the same for all levels of education. Some levels of education may cost more in terms of basic and supportive requirements. The purpose of this study is to identify determinants of household education expendiures and to examine whether education expenditures at education levels are affected by the same determinants. Data used in the study was obtained from the 2017 household budget survey, which was prepared by Turkey Statistical Institute. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an asymmetric extension to the tobit model by assuming that the error term follows a tilted-normal distribution. The new model, namely tilted-normal tobit model, can be an useful alternative to other skewed tobit models, such as the skewnormal and power-normal tobit models. The method of maximum likelihood is used for estimating the model parameters. We provide some simulation studies for different sample sizes and parameter settings. In addition, we perform residual and influence diagnostic analysis. Finally, we use American food consumption data to illustrate the better performance of the model introduced. [ABSTRACT FROM AUTHOR]

Censored regression ("Tobit") model is a special case of limited dependent variable regression model, and plays an important role in econometrics. Based on this model, all kinds of methods for variable or group variable selection have been developed and the corresponding shrinkage parameter estimates are widely studied. However, asymptotic distributions of the shrinkage estimates involve unknown nuisance parameters, such as density function of error term. To avoid estimating nuisance parameters, this paper presents a randomly weighting method to approximate to the asymptotic distribution of the shrinkage estimate. A computation procedure of random approximation is provided and asymptotic properties of the randomly weighting estimates are also obtained. The proposed methods are evaluated with extensively numerical studies and a women labor supply example. [ABSTRACT FROM AUTHOR]

In some applications of censored regression models, the distribution of the error terms departs significantly from normality, for instance, in the presence of heavy tails, skewness and/or atypical observation. In this paper we extend the censored linear regression model with normal errors to the case where the random errors follow a finite mixture of Student-t distributions. This approach allows us to model data with great flexibility, accommodating multimodality, heavy tails and also skewness depending on the structure of the mixture components. We develop an analytically tractable and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters, with standard errors as a by-product. The algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the truncated Student-t distributions. The efficacy of the method is verified through the analysis of simulated and real datasets. The proposed algorithm and methods are implemented in the new R package CensMixReg. [ABSTRACT FROM AUTHOR]

For the censored regression model, it is well-known that while classical least squares estimation yields biased and nonconsistent estimator, maximum likelihood estimator (MLE) is consistent and efficient. Tobit estimator (Tobit model) based on MLE of normal error distribution is commonly-used estimation method for estimating censored regression in econometric literature. However, while the Tobit estimator works well for normal error distribution, its estimates may be inefficient in the case of non-normal errors. To solve this problem, different error distributions for the censored regression model have been proposed and tested in the literature. In this study, we consider the censored regression model based on the generalized logistic distribution. Generalized logistic distribution is very flexible distribution and approximates normal distribution for the special parameter cases. The considered estimator for the censored regression is evaluated by means of a simulation study designed in different combination of various error distributions and sample sizes. The results of the simulation show that the estimator of the censored regression model based on the generalized logistic distribution provides good performance for different error distributions and it is particularly good for small sample sizes. Moreover, when it is compared to classical Tobit estimator, efficiency loss of the considered estimator is very small for normal error distribution. [ABSTRACT FROM AUTHOR]

In the framework of censored regression models the random errors are routinely assumed to have a normal distribution, mainly for mathematical convenience. However, this method has been criticized in the literature because of its sensitivity to deviations from the normality assumption. Here, we first establish a new link between the censored regression model and a recently studied class of symmetric distributions, which extend the normal one by the inclusion of kurtosis, called scale mixtures of normal (SMN) distributions. The Student-t, Pearson type VII, slash, contaminated normal, among others distributions, are contained in this class. A member of this class can be a good alternative to model this kind of data, because they have been shown its flexibility in several applications. In this work, we develop an analytically simple and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters, with standard errors as a by-product. The algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of certain truncated SMN distributions. The proposed algorithm is implemented in the R package SMNCensReg. Applications with simulated and a real data set are reported, illustrating the usefulness of the new methodology. [ABSTRACT FROM AUTHOR]

Payesh2010; 9: 77-82Accepted for publication: 23 February 2009[EPub a head of print-1 November 2009] Objective(s): Analysis of measles serum antibody responses is an important tool for evaluating the level of immunity in vaccinated people and for determining the factors which influence protective immunity. Usually antibody concentration values which are determined by quantitative assays are left censored, so standard analysis such as simple linear regression model may not be appropriate.Methods: Measles antibodies were measured using Enzyme Linked Immunosorbent Assay (ELISA) in 200 person aged 5-25 years before and after vaccination. A censored regression model proposed in order to reduce the effects of censored data in parameter estimating.Results: Twenty two persons (11 percent) of the sample had titer below detection limit which made censored data. 83 percent of children and teenagers (less than 16 years) and 88 percent of participant aged 16 and more were protective against measles disease, but there was no statistically significance between age and immunity (P= 0.4).Conclusion: Although the number of females who had protective antibodies against measles was less than males, they possessed an average titer 42 percent higher than males (without considering covariates). This proportion changed into 39% by using the censored regression model and adjusting with respect to “antibody titer before vaccination”. Both ratios showed higher vaccine- induced protection in females (P< 0.05).

In biomedical and epidemiological studies, often outcomes obtained are of mixed discrete and continuous in nature. Furthermore, due to some technical inconvenience or else, continuous responses are censored and also a few covariates cease to be observed completely. In this paper, we develop a model to tackle these complex situations. Our methodology is developed in a more general framework and provides a full-scale robust analysis of such complex models. The proposed robust maximum likelihood estimators of the model parameters are resistant to potential outliers in the data. We discuss the asymptotic properties of the robust estimators. To avoid computational difficulties involving irreducibly high-dimensional integrals, we propose a Monte Carlo method based on the Metropolis algorithm for approximating the robust maximum likelihood estimators. We study the empirical properties of these estimators in simulations. We also illustrate the proposed robust method using clustered data on blood sugar content from a clinical trial of individuals who were investigated for diabetes. [ABSTRACT FROM AUTHOR]

This article investigates the relationship between the star effect and match attendance in the National Basketball Association for the 2010-2011 and 2011-2012 seasons. We include five different definitions of star players, measures of short-run league-level competitive balance, and game uncertainty for two rivals in a Tobit regression model. The evidence shows that the appearance of stars increases home and road game attendance. A positive star externality on game attendance is found. [ABSTRACT FROM AUTHOR]

In this paper, we extend the censored linear regression model with normal errors to Student-terrors. A simple EM-type algorithm for iteratively computing maximum-likelihood estimates of the parameters is presented. To examine the performance of the proposed model, case-deletion and local influence techniques are developed to show its robust aspect against outlying and influential observations. This is done by the analysis of the sensitivity of the EM estimates under some usual perturbation schemes in the model or data and by inspecting some proposed diagnostic graphics. The efficacy of the method is verified through the analysis of simulated data sets and modelling a real data set first analysed under normal errors. The proposed algorithm and methods are implemented in the R packageCensRegMod. [ABSTRACT FROM AUTHOR]

This study aimed to predict the seasonal patterns of paralytic shellfish poison (PSP) level in the next 90 years based on a future climate scenario, Representative Concentration Pathway (RCP) 8.5. To achieve this goal, we constructed a censored regression model using seawater temperature, weekly change in seawater temperature, salinity, rainfall, insolation, shell species, and areas prone to red tide as potentially influential environmental factors on PSP level in the coastal areas of South Korea. The censored regression model is used instead of the ordinary regression model because the PSP data had a large portion of non-detectable (ND) data. All of the continuous environmental covariates had significant quadratic relationships with the PSP toxin level except insolation. These results indicated that there are favorable ranges of seawater temperature, weekly change in seawater temperature, salinity, and rainfall to PSP production. To predict the future PSP distribution, we plugged the environmental condition data under a future climate scenario, RCP 8.5 scenario, in the estimated regression model. In the future, it is expected that the highest frequency of shellfish poisoning outbreaks will occur during the earlier months in the year, such as February and March, whereas most outbreaks of shellfish poisoning have occurred in April and May during recent years in South Korea. [ABSTRACT FROM AUTHOR]

The electric power shortages in China the period from 2002 to 2007 had pervasive effects. How to measure the economic costs of power shortages becomes one of the central questions of interest to regulators, industrial users, and investors. The current study estimates the objective outage costs to industrial users using an adjusted Tobit model and survey data on 812 firms in six provinces in China during 2002–5. Empirical results show that objective outage costs are higher than the willingness to pay to avoid outages, but are lower than the willingness to accept to accept outages, implying that firms are loss averse. [ABSTRACT FROM PUBLISHER]

While much of the literature on cross-section dependence has focused on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spillovers and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful inferences and structural explanation for the strength of any interactions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Small sample performance is examined through a Monte Carlo study. Our methods are applied to a study of committee decision making within the Bank of England's Monetary Policy Committee. RÉSUMÉ Bien qu'une grande partie de la littérature sur la dépendance transversale se soit concentrée sur l'estimation des coefficients de régression dans le modèle sous-jacent, l'estimation et les inférences sur la magnitude et la force des retombées et des interactions ont été, en grande partie, ignorées. Parallèlement à ceci, ces inférences jouent un rôle important dans un grand nombre d'applications, ne serait-ce que parce qu'elles présentent des interprétations structurelles et fournissent des inférences utiles ainsi qu'une explication structurelle pour l'intensité des interactions. Dans la présente communication, nous proposons des méthodes GMM conues pour mettre à nu les interactions sous-jacentes (masquées) dans les réseaux et comités sociaux. On se penche tout particulièrement sur le modèle de régression à intervalle censuré, et on examine les performances de petits échantillons par le biais d'une étude Monte Carlo. Nos méthodes sont appliquées à une étude des prises de décision de comités au sein du comité de politique monétaire (Monetary Policy Committee) de la Banque d'Angleterre. EXTRACTO Aunque gran parte de la bibliografía sobre la dependencia transversal se ha centrado en estimar los coeficientes de regresión en el modelo subyacente, la estimación e inferencias de la magnitud y fortaleza de los spillovers e interacciones han sido ampliamente ignoradas. Al mismo tiempo, tales inferencias son importantes en muchas aplicaciones, entre otros motivos, porque tienen interpretaciones estructurales y proporcionan inferencias útiles y explicación estructural de la fortaleza de cualquier interacción. En este estudio, proponemos métodos GMM diseñados para descubrir interacciones subyacentes (ocultas) en redes sociales y comités. Se presta particular atención al modelo de regresión censurada a intervalos. Se examina el rendimiento de muestras pequeñas a través de un estudio de Monte Carlo. Nuestros métodos se aplican a un estudio de toma de decisiones de comité dentro del Comité de Política Monetaria del Banco de Inglaterra 摘要: 目前大量对跨部门依赖的研究文献都集中在估计模型的回归系数 , 估计和推导影响的幅度和强度 , 而相互影响几乎被忽略。但在同时 , 这种对相互影响的推导在许多场合十分重要 , 因为它不仅揭示内在结构 , 还可推测其他任何相互作用的强度并揭示内在结构。本文中 , 我们提出的GMM方法可以揭示社会网络和XX的隐藏相互作用。我们特别考虑了区间截尾回归模型。通过蒙特卡罗研究方法我们检验了小样本性能。应用本方法研究了英格兰银行货币政策委员会的决策制定。 [ABSTRACT FROM AUTHOR]

The main object of this article is to propose an extension of the tobit model for which the error distribution follows the power-normal distribution (Gupta and Gupta, 2008). Inference is dealt with by using the likelihood approach. Simulation studies and application to a real data set are used to demonstrate the usefulness of the extension. [ABSTRACT FROM PUBLISHER]

Interaction effects capture the impact of one explanatory variable x 1 on the marginal effect of another explanatory variable x 2. To explore interaction effects, the so-called interaction terms x 1 x 2 are typically included in estimation specifications. While in linear models the effect of a marginal change in the interaction term is equal to the interaction effect, this equality generally does not hold in nonlinear specifications (Ai and Norton, 2003). This article provides for a general derivation of marginal and interaction effects in both linear and nonlinear models and calculates the formulae of the marginal and interaction effects resulting from the Two-Part Model (2PM), a commonly employed censored regression model. Drawing on a survey of automobile use from Germany, we illustrate several subtleties inherent to the substantive interpretation of interaction effects gleaned from nonlinear models, such as the 2PM. [ABSTRACT FROM AUTHOR]

The goal of this paper is to introduce a partially adaptive estimator for the censored regression model based on an error structure described by a mixture of two normal distributions. The model we introduce is easily estimated by maximum likelihood using an EM algorithm adapted from the work of Bartolucci and Scaccia (Comput Stat Data Anal 48:821-834, ). A Monte Carlo study is conducted to compare the small sample properties of this estimator to the performance of some common alternative estimators of censored regression models including the usual tobit model, the CLAD estimator of Powell (J Econom 25:303-325, ), and the STLS estimator of Powell (Econometrica 54:1435-1460, ). In terms of RMSE, our partially adaptive estimator performed well. The partially adaptive estimator is applied to data on wife's hours worked from Mroz (). In this application we find support for the partially adaptive estimator over the usual tobit model. [ABSTRACT FROM AUTHOR]

This paper introduces and applies an EM algorithm for the maximum-likelihood estimation of a latent class version of the grouped-data regression model. This new model is applied to examine the effects of college athletic participation of females on incomes. No evidence for an “athlete” effect in the case of females has been found in the previous work by Long and Caudill [12], Henderson et al. [10], and Caudill and Long [5]. Our study is the first to find evidence of a lower wage for female athletes. This effect is present in a regime characterizing 42% of the sample. Further analysis indicates that female athletes in many otherwise low-paying jobs actually get paid less than non-athletes. [ABSTRACT FROM AUTHOR]

This paper presents a new- alternative semiparametric estimation approach for censored regression models based on the survival function of the censored dependent variable, and gives a simulation study on the constructed estimator in comparison with HP estimators, which shows that the estimator has a good performance in small samples. [ABSTRACT FROM AUTHOR]

The goal of this paper is to introduce a partially adaptive estimator for the grouped-data regression model based on an error structure described by a mixture of two normal distributions. The model we introduce is easily estimated by maximum likelihood using the EM algorithm adapted from the work of Bartolucci and Scaccia (Comput Stat Data Anal 48:821–834, 2005). The partially adaptive estimator is applied to data used by Long and Caudill (Rev Econ Stat 73:525–531, 1991) to examine the impact of intercollegiate athletics on income. We estimate a variation of the original regression model and find that there is a considerable financial advantage for those male athletes now working in business management. This finding is consistent with the idea that athletes acquire team-building and organizational skills that are helpful in business. [ABSTRACT FROM AUTHOR]

In a recent volume of this journal, Holden [Testing the normality assumption in the Tobit Model, J. Appl. Stat. 31 (2004) pp. 521-532] presents Monte Carlo evidence comparing several tests for departures from normality in the Tobit Model. This study adds to the work of Holden by considering another test, and several information criteria, for detecting departures from normality in the Tobit Model. The test given here is a modified likelihood ratio statistic based on a partially adaptive estimator of the Censored Regression Model using the approach of Caudill [A partially adaptive estimator for the Censored Regression Model based on a mixture of normal distributions, Working Paper, Department of Economics, Auburn University, 2007]. The information criteria examined include the Akaike's Information Criterion (AIC), the Consistent AIC (CAIC), the Bayesian information criterion (BIC), and the Akaike's BIC (ABIC). In terms of fewest 'rejections' of a true null, the best performance is exhibited by the CAIC and the BIC, although, like some of the statistics examined by Holden, there are computational difficulties with each. [ABSTRACT FROM AUTHOR]

Abstract: In the last few decades, changes in consumer habits have been observed in household economies. These habits now focus on large retail chains and shopping centers out-of-town rather than independent retailers sited, for the most part, in shopping districts or in the town center. One factor in this trend is the retailer’s accessibility to the customer. Because of this relationship, the retail market share is affected by the development of an efficient transport system, such as the Metro. This paper examines both short- and long-term positive and negative interactions between the retail sector and the introduction of the Metro using empirical evidence from three middle-sized Spanish cities (Bilbao, Málaga and Seville). It also analyzes how retailers assess the influence of a nearby Metro station on their business. Finally, the conclusion is drawn that a new planning model for urban transportation infrastructure works is beginning to prevail with the backing of smallbusiness association lobbies. This model seeks to minimize the effect of works-related negative externalities on urban retail fabrics but can never guarantee a happy ending for independent stores. [Copyright &y& Elsevier]

Censored regression (“Tobit”) models have been in common use, and their linear hypothesis testings have been widely studied. However, the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters. In this paper, we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic. It is shown that, under both the null and local alternative hypotheses, conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic. Therefore, the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters. At the same time, we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model. Simulation studies illustrate that the performance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the second-order least squares estimation, which is an extension of the ordinary least squares method, for censored regression models where the error term has a general parametric distribution (not necessarily normal). The strong consistency and asymptotic normality of the estimator are derived under fairly general regularity conditions. We also propose a computationally simpler estimator which is consistent and asymptotically normal under the same regularity conditions. Finite sample behavior of the proposed estimators under both correctly and misspecified models are investigated through Monte Carlo simulations. The simulation results show that the proposed estimator using optimal weighting matrix performs very similar to the maximum likelihood estimator, and the estimator with the identity weight is more robust against the misspecification. [Copyright &y& Elsevier]

A brief survey of estimation of parameters in a censored regression model (known as the Tobit model) and some details of the properties of LAD (least absolute deviation) estimates and tests of significance of linear hypotheses are given. [ABSTRACT FROM AUTHOR]

In this article we examine the potential effect of market structure on hospital technical efficiency as a measure of performance controlled by ownership arid regulation. This study is relevant to provide an evaluation of the potential effects of recommended and initiated deregulation policies in order to promote market reforms in the context of a European National Health Service. Our goal was reached through three main empirical stages. Firstly, using patient origin data from hospitals in the region of Catalonia in 1990, we estimated geographic hospital markets through the Elzinga-Hogarty approach. based on patient flows. Then we measured the market level of concentration using the Herfindahl-Hirschman index. Secondly, technical arid scale efficiency scores for each hospital was obtained specifying a Data Envelopment Analysis. According to the data nearly two-thirds of the hospitals operate under the production frontier with an average efficiency score of 0.841. Finally, the determinants of the efficiency scores were investigated using a censored regression model. Special attention was paid to test the hypothesis that there is an efficiency improvement in more competitive markets. The results suggest that the number of competitors in the market contributes positively to technical efficiency and there is some evidence that the differences in efficiency scores are attributed to several environmental factors such as ownership, market structure and regulation effects. [ABSTRACT FROM AUTHOR]