Frontier production functions

This paper summarizes the literature on frontier production and cost functions and on their relationship to the problem of efficiency measurement.     

Frontier production functions

This paper summarizes the literature on frontier production and cost functions and on their relationship to the problem of efficiency measurement.     

GMM Estimation with persistent panel data: an application to production functions

This paper considers the estimation of Cobb-Douglas production functions using panel data covering a large sample of companies observed for a small number of time periods. GMM estimatorshave been found to produce large finite-sample biases when using the standard first-differenced estimator. These biases can be dramatically reduced by exploiting reasonable stationarity restrictions on the initial conditions process. Using data for a panel of R&Dperforming US manufacturing companies we find that the additional instruments used in our extended GMM estimator yield much more reasonable parameter estimates.     

GMM Estimation with persistent panel data: an application to production functions

This paper considers the estimation of Cobb-Douglas production functions using panel data covering a large sample of companies observed for a small number of time periods. GMM estimatorshave been found to produce large finite-sample biases when using the standard first-differenced estimator. These biases can be dramatically reduced by exploiting reasonable stationarity restrictions on the initial conditions process. Using data for a panel of R&Dperforming US manufacturing companies we find that the additional instruments used in our extended GMM estimator yield much more reasonable parameter estimates.     

Large deviation properties for empirical quantile-type production functions

Three examples of point processes are given where the autocovariance decays in an inverse power law, but which are not fractal.     

Solving endogeneity problems in multilevel estimation: an example using education production functions

This paper explores endogeneity problems in multilevel estimation of education production functions. The focus is on level 2 endogeneity which arises from correlations between student characteristics and omitted school variables. Theses correlations are mainly the result of student stratification between schools. From an econometric point of view, the correlations between student and school characteristics imply that the omission of some variables may generate endogeneity bias. Therefore, an estimation approach based on the Mundlak [20 Mundlak, Y. 1978. On the pooling of time series and cross-sectional data. Econometrica, 46: 69–86. [Crossref], [Web of Science ®] , [Google Scholar]] technique is developed in order to tackle bias and to generate consistent estimates. Note that our analysis can be extended to any multilevel-structured data (students nested within schools, employees within firms, firms within regions, etc). The entire analysis is undertaken in a comparative context between three countries: Germany, Finland and the UK. Each one of them represents a particular system. For instance, Finland is known for its extreme comprehensiveness, Germany for early selection and the UK for its liberalism. These countries are used to illustrate the theory and to prove that the level of bias arising from omitted variables varies according to the characteristics of education systems.     

Stochastic Production Frontier and Technical Inefficiency: A Sensitivity Analysis

The present paper focuses attention on the sensitivity of technical inefficiency to most commonly used one-sided distributions of the inefficiency error term, namely the truncated normal, the half-normal, and the exponential distributions. A generalized version of the half-normal, which does not embody the zero-mean restriction, is also explored. For each distribution, the likelihood function and the counterpart of the estimator of technical efficiency are explicitly stated (Jondrow, J., Lovell, C. A. K., Materov, I. S., Schmidt, P. ([1982]), On estimation of technical inefficiency in the stochastic frontier production function model, J. Econometrics19:233-238). Based on our panel data set, related to Tunisian manufacturing firms over the period 1983-1993, formal tests lead to a strong rejection of the zero-mean restriction embodied in the half normal distribution. Our main conclusion is that the degree of measured inefficiency is very sensitive to the postulated assumptions about the distribution of the one-sided error term. The estimated inefficiency indices are, however, unaffected by the choice of the functional form for the production function.     

Detecting Outliers in Large Medical Records: Using a Frontier Model

In this study, we want to detect the outliers from large medical records and exclude them because outliers will influence the accuracy and efficiency of the analysis. We compare the traditional method and the method from frontier model. In this study, we want to detect the outliers of cost and length of stay for pneumonia patients from HCUP 2002 and 2003.     

Comment: Administrative Statistics: A BLS Perspective

A semiparametric, information-based estimator is used to estimate strategies in prices and advertising for Coca-Cola and Pepsi-Cola. Separate strategies for each firm are estimated with and without restrictions from game theory. These information/entropy estimators are consistent and efficient. These estimates are used to test theories about the strategies of firms and to see how changes in incomes or factor prices affect these strategies.     

Comment

The article provides detailed and accurate illustrations of Bayesian analysis of DSGE models that are likely to be used increasingly in support of central bank policy making. These comments identify a dozen aspects of these methods, discussing how their application and improvement can contribute to effective support of policy.     

Comment

A vector autoregression is a reduced-form representation and therefore would be expected to change when any structural equation in the system changes, regardless of whether economic decisions are forward-looking. Even so, a dynamic simulation of a model with unit roots will exhibit large cumulative errors, making it difficult to detect whether a structural change has indeed occurred.     

Comment

Some considerations relating to the post–data selection of models are discussed. These include some difficulties with orthodox theory, implementation of the likelihood principle, and Bayesian tests of hypotheses.     

Comment

Bonferroni inequalities often provide tight upper and lower bounds for the probability of a union of events. The bounds are especially useful when this probability is difficult to compute exactly. There are situations, however, in which the Bonferroni approach gives very poor results. An example is given in which the upper and lower Bonferroni bounds are far from the probability they seek to approximate and successive bounds do not converge. Even an improved first upper Bonferroni bound may not be close to the probability of the union of events.