According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

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Modern Applied Statistics With S

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

http://d1c25a6gwz7q5e.cloudfront.net/papers/67.pdf

Efficiency of financial institutions: International survey and directions for future research

Estimation and inference in two-stage, semi-parametric models of production processes

Conventional approaches for inference about efficiency in parametric stochastic frontier (PSF) models are based on percentiles of the estimated distribution of the one-sided error term, conditional on the composite error. When used as prediction intervals, coverage is poor when the signal-to-noise ratio is low, but improves slowly as sample size increases. We show that prediction intervals estimated by bagging yield much better coverages than the conventional approach, even with low signal-to-noise ratios. We also present a bootstrap method that gives confidence interval estimates for (conditional) expectations of efficiency, and which have good coverage properties that improve with sample size. In addition, researchers who estimate PSF models typically reject models, samples, or both when residuals have skewness in the "wrong" direction, i.e., in a direction that would seem to indicate absence of inefficiency. We show that correctly specified models can generate samples with "wrongly" skewed residuals, even when the variance of the inefficiency process is nonzero. Both our bagging and bootstrap methods provide useful information about inefficiency and model parameters irrespective of whether residuals have skewness in the desired direction.

Inferences from Cross-Sectional, Stochastic Frontier Models

https://www.tse-fr.eu/sites/default/files/medias/doc/by/vanhems/statistical.pdf

Statistical inference for DEA estimators of directional distances

Non-parametric data envelopment analysis (DEA) estimators based on linear programming methods have been widely applied in analyses of productive efficiency. The distributions of these estimators remain unknown except in the simple case of one input and one output. This paper derives the asymptotic distribution of DEA estimators under variable returns-to-scale. In addition, two bootstrap procedures (one based on sub-sampling, the other based on smoothing) are shown to provide consistent inference. The smooth bootstrap requires smoothing the irregularly-bounded density of inputs and outputs as well as smoothing of the DEA frontier estimate. Both bootstrap procedures allow for dependence of the inefficiency process on output levels and the mix of inputs in the case of input-oriented measures, or on inputs levels and the mix of outputs in the case of output-oriented measures.

Asymptotics for DEA estimators in non-parametric frontier models

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units, until now no methods exist for making consistent inference about differences in mean efficiency across groups of producers or for testing hypotheses about model structure such as returns to scale or convexity of the production set. We use central limit theorem results from our previous work to develop additional theoretical results permitting consistent tests of model structure and provide Monte Carlo evidence on the performance of the tests in terms of size and power. In addition, the variable returns to scale version of the DEA estima...

/pdf/testing-hypotheses-in-nonparametric-models-of-production-55dv5uqpiq.pdf

Testing Hypotheses in Nonparametric Models of Production

This study analyzes the impact of policy variables and other factors on hospital technical inefficiency in the US. Distance functions are used to estimate technical efficiency for each hospital relative to contemporaneous technologies. The resulting panel of efficiency estimates are then regressed on exogenous factors to gain insight into various issues impacting the debate over health-care reform in the US. Methodologically, we extend previous work by recognizing data envelopment analysis (DEA) efficiency scores as estimates, and by dealing with a censoring problem at the estimated technology frontier. We use annual data on US hospitals operated by the US Department of Veterans Affairs (VA), state and local governments, and for-profit as well as nonprofit organizations from 1985 to 1988.

Paul W. Wilson

Papers

Inferences from Cross-Sectional, Stochastic Frontier Models

Statistical inference for DEA estimators of directional distances

Asymptotics for DEA estimators in non-parametric frontier models

Testing Hypotheses in Nonparametric Models of Production

Variation In Inefficiency Among Us Hospitals