A generalisation of the Farrell cost efficiency measure applicable to non-fully competitive settings

This paper enhances cost efficiency measurement methods to account for situations where the input prices can depend on negotiation or tend to qualitatively differentiate the resources available at each decision making unit. In these circumstances, there are some shortcomings in the cost efficiency measure described in the data envelopment analysis (DEA) literature. This paper proposes new models and measures for cost efficiency evaluation that overcome the limitations of the existing DEA models. The applicability of the measures developed is illustrated in the context of the analysis of bank branch performance.     

Identifying Social Interactions Through Conditional Variance Restrictions

This paper proposes a new method for identifying social interactions using conditional variance restrictions. The method provides a consistent estimate of the social multiplier when social interactions take the “linear‐in‐means” form (Manski (1993)). When social interactions are not of the linear‐in‐means form, the estimator, under certain conditions, continues to form the basis of a consistent test of the no social interactions null with correct large sample size. The methods are illustrated using data from the Tennessee class size reduction experiment Project STAR. The application suggests that differences in peer group quality were an important source of individual‐level variation in the academic achievement of Project STAR kindergarten students.     

Contracting Under Vendor Managed Inventory Systems Using Holding Cost Subsidies

Vendor managed inventory systems are becoming increasingly popular. An important issue in implementing a vendor managed inventory scheme is the contracting terms that dictate the ownership of the inventory and the responsibility of inventory replenishment decisions. Thus the performance of a vendor managed system crucially depends on these terms and on how inventory‐related costs are shared in a supply chain. We consider a system where a manufacturer supplies a single product to a retailer who faces random demand in a competitive market. The retailer incurs a fixed cost per order, inventory holding cost, and a penalty cost for a stockout (unsatisfied demand is back‐ordered). Further, the manufacturer incurs a penalty cost when there is a stockout at the retailer and a fixed replenishment cost. We assume that the players are rational and act noncooperatively. We compare the performance of retailer managed inventory systems, where the retailer places orders and makes replenishment decisions, with vendor managed inventory systems, wherein the vendor or manufacturer makes inventory and replenishment decisions. Specifically, in the vendor managed inventory system, we propose and evaluate holding cost subsidy‐type contracts on inventories offered by the retailer to improve system performance. We evaluate this contract in the context of three widely used inventory systems—deterministic economic order quantity, continuous review (Q, r) policies, and periodic review policies—and show when such contracts may improve channel performance.     

Corporate Social and Financial Performance: The Role of Size,Industry, Risk,R&D and Advertising Expenses as Control Variables

This article investigates the role of commonly specified control variables in moderating the relationship between corporate social performance (CSP) and corporate financial performance (CFP). In addition, there are separate measures for positive (strengths) social actions, and for negative (concerns) social actions. The results support the positive relationship between CSP and CFP. The best model, as determined using factorial analysis of variance, is one which has the following control variables: size, industry, risk, and research and development expenditures. In examining the CSP/CFP relationship, researchers must control for these variables, in order to properly specify the model.     

Nonparametric Instrumental Regression

The focus of this paper is the nonparametric estimation of an instrumental regression function ϕ defined by conditional moment restrictions that stem from a structural econometric model E[Y−ϕ(Z)|W]=0, and involve endogenous variables Y and Z and instruments W. The function ϕ is the solution of an ill‐posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyzes identification and overidentification of this model, and presents asymptotic properties of the estimated nonparametric instrumental regression function.     

Spatially varying dynamic coefficient models

In this work we present a flexible class of linear models to treat observations made in discrete time and continuous space, where the regression coefficients vary smoothly in time and space. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial heterogeneity in both dimensions. We describe how to perform inference for this class of models and also how to perform forecasting in time and interpolation in space, using simulation techniques. The performance of the algorithm to estimate the parameters of the model and to perform prediction in time is investigated with simulated data sets. The proposed methodology is used to model pollution levels in the Northeast of the United States.     

A class of count models and a new consistent test for the Poisson distribution

We define a class of count distributions which includes the Poisson as well as many alternative count models. Then the empirical probability generating function is utilized to construct a test for the Poisson distribution, which is consistent against this class of alternatives. The limit distribution of the test statistic is derived in case of a general underlying distribution, and efficiency considerations are addressed. A simulation study indicates that the new test is comparable in performance to more complicated omnibus tests.     

Statistical inference for start-up demonstration tests with rejection of units upon observing d failures

In this paper, we consider the statistical inference for the success probability in the case of start-up demonstration tests in which rejection of units is possible when a pre-fixed number of failures is observed before the required number of consecutive successes are achieved for acceptance of the unit. Since the expected value of the stopping time is not a monotone function of the unknown parameter, the method of moments is not useful in this situation. Therefore, we discuss two estimation methods for the success probability: (1) the maximum likelihood estimation (MLE) via the expectation-maximization (EM) algorithm and (2) Bayesian estimation with a beta prior. We examine the small-sample properties of the MLE and Bayesian estimator. Finally, we present an example to illustrate the method of inference discussed here.