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The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

Microsimulation is a technique that uses the capacity of modern computers to make microunits act and interact in such a way that it is possible to aggregate to the level of interest. A microsimulation model can be seen as a set of rules, which operates on a sample of microunits such as individuals, households, and firms. Each microunit is defined and characterized by a set of properties (variables) and as the model is simulated these properties are updated for each and every microunit. The model might simply be a set of deterministic rules such as the income tax rules of a country operating on a sample of taxpayers, and used to compute the distribution of after-tax income, the aggregate income tax revenue, or other fiscal entities of interest. But the model could also include behavioral assumptions usually formulated as stochastic models. Examples are fertility models, models for household formation and dissolution, labor supply, and mobility.

Tail-dependence evolution for the symmetrized Joe–Clayton copula is proposed to depend on an exponentially weighted moving average (EWMA) of the absolute difference in probability integral transforms. Using these dynamics, time-varying tail dependence between bank and insurance equity prices is assessed in a parametric copula, generalized autoregressive conditional heteroscedastic framework. The results suggest a relatively long lag and support the EWMA lag structure as an effective estimation vehicle. Tail dependence is shown often to tend higher during periods of market stress.

This paper develops a framework for understanding history as a source of competitive advantage. Prior research suggests that some firms enjoy preferential access to resources as a result of their past. Historians, by contrast, understand past events as more than an objective account of reality. History also has an interpretive function. History is a social and rhetorical construction that can be shaped and manipulated to motivate, persuade, and frame action, both within and outside an organization. Viewed as a malleable construct, the capacity to manage history can, itself, be a rare and inimitable resource.