Motivated by the growing discussion on the resemblance of multilevel marketing schemes to pyramid scams, we compare the two phenomena based on their underlying compensation structures. We show that a company can design a pyramid scam to exploit a network of agents with coarse beliefs and that...

Motivated by the growing discussion on the resemblance of multilevel marketing schemes to pyramid scams, we compare the two phenomena based on their underlying compensation structures. We show that a company can design a pyramid scam to exploit a network of agents with coarse beliefs and that...

This study compares the size and power of autoregressive conditional heteroskedasticity (ARCH) tests that are robust to the presence of a misspecified conditional mean. The approaches employed are based on two nonparametric regressions for the conditional mean: an ARCH test with a...

We model an individual who wants to learn about a state of the world. The individual has a prior belief, and has data which consists of multiple forecasts about the state of the world. Our key assumption is that the decision maker identifies explanations that could have generated this data and...

We model an individual who wants to learn about a state of the world. The individual has a prior belief, and has data which consists of multiple forecasts about the state of the world. Our key assumption is that the decision maker identifies explanations that could have generated this data and...

This study compares the size and power of autoregressive conditional heteroskedasticity (ARCH) tests that are robust to the presence of a misspecified conditional mean. The approaches employed are based on two nonparametric regressions for the conditional mean: an ARCH test with a...

We study learning and information acquisition by a Bayesian agent whose prior belief is misspecified in the sense that it assigns probability 0 to the true state of the world. At each instant, the agent takes an action and observes the corresponding payoff, which is the sum of a fixed but...

This paper proposes an entropy-based approach for aggregating information from misspecified asset pricing models. The statistical paradigm is shifted away from parameter estimation of an optimally selected model to stochastic optimization based on a risk function of aggregation across models....