Modified Nonradial Supper Efficiency Models

Ranking Efficient Decision Making Units (DMUs) are an important issue in Data Envelopment Analysis (DEA). This is one of the main areas for the researcher. Different methods for this purpose have been suggested. Appearing nonzero slack in optimal solution makes the method problematic. In this paper, we modify the nonradial supper efficiency model to remove this difficulty. Some numerical examples are solved by modified model

Modelling generalized firms' restructuring using inverse DEA

The key consideration for firms’ restructuring is improving their operational efficiencies. Market conditions often offer opportunities or generate threats that can be handled by restructuring scenarios through consolidation, to create synergy, or through split, to create reverse synergy. A generalized restructuring refers to a move in a business market where a homogeneous set of firms, a set of pre-restructuring decision making units (DMUs), proceed with a restructuring to produce a new set of post-restructuring entities in the same market to realize efficiency targets. This paper aims to develop a novel inverse Data Envelopment Analysis based methodology, called GInvDEA (Generalized Inverse DEA), for modeling the generalized restructuring. Moreover, the paper suggests a linear programming model that allows determining the lowest performance levels, measured by efficiency that can be achieved through a given generalized restructuring. An application in banking operations illustrates the theory developed in the paper

A classification of DEA models when the internal structure of the Decision Making Units is considered

We classify the contributions of DEA literature assessing Decision Making Units (DMUs) whose internal structure is known. Starting from an elementary framework, we define the main research areas as shared flow, multilevel and network models, depending on the assumptions they are subject to. For each model category, the principal mathematical formulations are introduced along with their main variants, extensions and applications. We also discuss the results of aggregating efficiency measures and of considering DMUs as submitted to a central authority that imposes constraints or targets on them. A common feature among the several models is that the efficiency evaluation of the DMU depends on the efficiency values of its subunits thereby increasing the discrimination power of DEA methodology with respect to the black box approach

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

A Modification to the WPC Model

We modify the Barclay and Warner (1993) Weighted Price Contribution model – which measures market participants’ contribution to price discovery process – to incorporate price movements that extend beyond the final price. We validate our model with an empirical illustration

Monitoring the Foreign Exchange Rate Benchmark Fix

We develop a Manipulation Index (ManIx) that captures the potential manipulation intention of dealers during the World Markets/Reuters (WMR) benchmark (London Close) period at 4 pm London time through a unique algorithm and simulation. The application of this model (using a dataset with dealers’ identities) can identify banks that are prone to potential manipulative behavior. The results concerning the identified banks are validated by the regulatory investigations. Implementation of this algorithm allows regulators better direct their limited resources towards more targeted in-depth investigation