883 research outputs found
Conditional Nonparametric Frontier Models for Convex and Non Convex Technologies: a Unifying Approach
No full textThe explanation of productivity differentials is very important to identify the economic conditions that create inefficiency and to improve managerial performance. In literature two main approaches have been developed: one-stage approaches and two-stage approaches. Daraio and Simar (2003) propose a full nonparametric methodology based on conditional FDH and conditional order-m frontiers without any convexity assumption on the technology. On the one hand, convexity has always been assumed in mainstream production theory and general equilibrium. On the other hand, in many empirical applications, the convexity assumption can be reasonable and sometimes natural. Leading by these considerations, in this paper we propose a unifying approach to introduce external-environmental variables in nonparametric frontier models for convex and non convex technologies. Developing further the work done in Daraio and Simar (2003) we introduce a conditional DEA estimator, i.e., an estimator of production frontier of DEA type conditioned to some external-environmental variables which are neither inputs nor outputs under the control of the producer. A robust version of this conditional estimator is also proposed. These various measures of efficiency provide also indicators of convexity. Illustrations through simulated and real data (mutual funds) examples are reported.Convexity, External-Environmental Factors, Production Frontier, Nonparametric Estimation, Robust Estimation.
[Leopold Marx picture postcard].
No full textPicture postcard with photograph of author Leopold Marx.Author, born 1889 in Bad Cannstatt. Emigrated from Germany to Israel in 1939. Died 1983 in Israel.Processed for digitizationSent for digitizationReturned from digitizationLinked to online manifestationdigitize
Stochastic FDH/DEA estimators for frontier analysis
No full textIn this paper we extend the work of Simar (2007) introducing noise in nonparametricfrontier models. We develop an approach that synthesizes the best features of theb two main methods in the estimation of production efficiency. Specifically, our approach first allows for statistical noise, similar to Stochastic Frontier Analysis (even in a more flexible way), and second, it allows modelling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods (DEA, FDH, etc. . . ). The methodology is based on the theory of local maximum likelihood estimation and extends recent works of Park, Kumbhakar, Simar and Tsionas (2007) and Park, Simar and Zelenyuk (2006). Our method is suitable for modelling and estimation of the marginal effects onto inefficiency level jointly with estimation of marginal effects of input. The approach is robust to heteroskedastic cases and to various (unknown) distributions of statistical noise and inefficiency, despite assuming simple anchorage models. The method also improves DEA/FDH estimators, by allowing them to be quite robust to statistical noise and especially to outliers, which were the main problems of the original DEA/FDH. The procedure shows great performance for various simulated cases and is also illustrated for some real data sets
On testing equality of distributions of technical efficiency scores
No full textThe challenge of the econometric problem in production efficiency analysis is that the very efficiency scores to be analyzed are unobserved. Recently, statistical properties have been discovered for a class of estimators popular in the literature, known as data envelopment analysis (DEA) approach. This opens a wide range of possibilities for a well-grounded statistical inference about the true efficiency scores from their DEA-estimates. In this paper we investigate possibility of using existing tests for equality of two distributions for such a context. Considering statistical complications pertinent to our context, we consider several approaches to adapt the Li (1996) test to the context and explore their performance in terms of the size and the power of the test in various Monte Carlo experiments. One of these approaches showed good performance both in the size and in the power, thus encouraging for its wide use in empirical studies.Kernel Density Estimation and Tests, Bootstrap, DEA
Efficiency and benchmarking with directional distances: a data-driven approach
No full textIn efficiency analysis the assessment of the performance of Decision-Making Units (DMUs) relays on the selection of the direction along which the distance from the efficient frontier is measured. Directional Distance Functions (DDFs) represent a flexible way to gauge the inefficiency of DMUs. Permitting the selection of a direction towards the efficient frontier is often useful in empirical applications. As a matter of fact, many papers in the literature have proposed specific DDFs suitable for different contexts of application. Nevertheless, the selection of a direction implies the choice of an efficiency target which is imposed to all the analysed DMUs. Moreover, there exist many situations in which there is no a priori economic or managerial rationale to impose a subjective efficiency target. In this paper we propose a data-driven approach to find out an ‘objective’ direction along which to gauge the inefficiency of each DMU. Our approach permits to take into account for the heterogeneity of DMUs and their diverse contexts that may influence their input and/or output mixes. Our method is also a data-driven technique for benchmarking each DMU. We describe how to implement our framework and illustrate its usefulness with simulated and real data sets
Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach
No full textThis paper proposes a general formulation of a nonparametric frontier model introducingexternal environmental factors that might influence the production process butare neither inputs nor outputs under the control of the producer. A representation isproposed in terms of a probabilistic model which defines the data generating process.Our approach extends the basic ideas from Cazals, Florens and Simar (2002) to thefull multivariate case. We introduce the concepts of conditional efficiency measure andof conditional efficiency measure of order-m. Afterwards we suggest a practical wayfor computing the nonparametric estimators. Finally, a simple methodology to investigatethe influence of these external factors on the production process is proposed.Numerical illustrations through some simulated examples and through a real data seton Mutual Funds show the usefulness of the approach.production function, frontier, nonparametric estimation, environmental factors,robust estimation.
To Smooth or Not to Smooth? The Case of Discrete Variables in Nonparametric Regressions
No full textIn a seminal paper, Racine and Li, (Journal of Econometrics, 2004) introduce a tool which admits discrete and categorical variables as regressors in nonparametric regres- sions. The method is similar to the smoothing techniques for continuous regressors but uses discrete kernels. In the literature, it is generally admitted that it is always better to smooth the discrete variables. In this paper we investigate the potential problem linked to the bandwidths selection for the continuous variable due to the presence of the discrete variables. We find that in some cases, the performance of the resulting regression estimates may be deteriorated by smoothing the discrete variables in the way addressed so far in the literature, and that a fully separate estimation (without any smoothing of the discrete variable) may provide significantly better results, and we explain why this may happen. The problem being posed, we then suggest how to use the Racine and Li approach to overcome these difficulties and to provide estimates with better performances. We investigate through some simulated data sets and by more ex- tensive Monte-Carlo experiments the performances of all the proposed approaches and we find that, as expected, our suggested approach has the best performances. We also briefly illustrate the consequences of these issues on the estimation of the derivatives of the regression. Finally, we exemplify the phenomenon with an empirical illustration. Our main objective is to warn the practitioners of the potential problems posed by smoothing discrete variables by using the so far available softwares and to suggest a safer approach to implement the procedure.
Studio portrait of Leopold Kompert.
No full textAuthor, born 1822 in Muenchengraetz (Bohemia). Died 1886 in Vienna.Digital Imag
Local Likelihood Estimation of Truncated Regression and Its Partial Derivatives: Theory and Application
No full textIn this paper we propose a very flexible estimator in the context of truncated regression that does not require parametric assumptions. To do this, we adapt the theory of local maximum likelihood estimation. We provide the asymptotic results and illustrate the performance of our estimator on simulated and real data sets. Our estimator performs as good as the fully parametric estimator when the assumptions for the latter hold, but as expected, much better when they do not (provided that the curse of dimensionality problem is not the issue). Overall, our estimator exhibits a fair degree of robustness to various deviations from linearity in the regression equation and also to deviations from the specification of the error term. So the approach shall prove to be very useful in practical applications, where the parametric form of the regression or of the distribution is rarely known.Nonparametric Truncated Regression, Local Likelihood
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