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We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

It is widely documented that while contemporaneous spot and forward financial prices trace each other extremely closely, their difference is often highly persistent and the conventional cointegration tests may suggest lack of cointegration. This chapter studies the possibility of having cointegrated errors that are characterized simultaneously by high persistence (near-unit root behavior) and very small (near zero) variance. The proposed dual parameterization induces the cointegration error process to be stochastically bounded which prevents the variables in the cointegrating system from drifting apart over a reasonably long horizon. More specifically, this chapter develops the appropriate asymptotic theory (rate of convergence and asymptotic distribution) for the estimators in unconditional and conditional vector error correction models (VECM) when the error correction term is parameterized as a dampened near-unit root process (local-to-unity process with local-to-zero variance). The important differences in the limiting behavior of the estimators and their implications for empirical analysis are discussed. Simulation results and an empirical analysis of the forward premium regressions are also provided.

We demonstrate in a laboratory experiment that the effectiveness of performance-contingent incentives is inversely related to risk-aversion levels. For about 16.5% of participants, performance fails to improve under performance-pay, and the probability of such failure increases with risk-aversion. This phenomenon works in part through the reduced effort level of more risk-averse individuals when effort level is positively correlated with risk exposure. It is also associated with higher self-reported levels of stress by more risk-averse people working under performance-contingent pay. We find no evidence of such stress causing decrements in the quality of effort affecting performance after controlling for effort level. However, controlling for effort, more risk-averse participants perform better under a fixed salary, leaving less room for improvement under performance-pay.

This chapter explores the development of the dangerous, sexualized fembot archetype in science-fiction film and television, drawing a line from the robot Maria in Fritz Lang's Metropolis (1927) to contemporary versions of the archetype.

Primarily, this chapter outlines how this historically villainous trope has been augmented and redefined in twenty-first Century posthuman science-fiction texts Ex Machina (Alex Garland, 2014) and Westworld (Joy et al., 2016 –). Both feature fembot characters who are central to the narrative, and can be defined as both villainous at times, but who also occupy the position of arguable sympathetic protagonists. In part, this redefinition can be argued as more a reflection of a Western hegemonic shift towards feminist values. Nevertheless, there have been criticisms of the male gaze present in both and of the emphasis on female suffering.

As oblique texts for an 18–35 audience, both Ex Machina and Westworld ask more questions than they answer. Through textual analysis and with reference to relevant scholarship, this chapter considers the impact of audience and institution on representation, the interplay between genre conventions and the presentation of the archetype as well as a considering how both offer different treatment of intersectional androids.