We study the problem of potentially spurious attribution of dependence in moderate to large samples, where both the number of variables and length of variable observations are growing. We approach this question of double asymptotics from both theoretical and empirical perspectives. For...

Extreme events affect both the real economy and financial markets, and it is valuable to understand their interrelationship. We analyze the likelihood of crises in the macroeconomy and in financial markets. We compare rare disaster data from Barro and Jin (2011), crisis data from Reinhart and...

Where do central bank priors come from and how do policymakers evaluate a model before empirical probabilities are available? To address these questions, we analyze two central banks that choose priors about a rare disaster with the help of expert policy teams. Policymakers are misspecification...

Decision theory has relatively little to say about the formal choice of priors. We pursue the issue of prior choice in a framework that builds on consumer theory. We analyze discovery decisions of a reasoner, in an environment of hypotheses with heterogeneous, subjective plausibility. We...

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We analyze whether executive compensation reflects firm default risk, measured by distance to default of Merton (1974). Using a large panel of firms, we explore several empirical frameworks. In least squares, fixed effects and quantile regression settings, default risk and volatility possess...

Where do prior beliefs come from and how do decisionmakers ascribe confidence in a theory before probabilities are available? To address these questions, we outline an axiomatic approach for selection of priors, where the reasoner is misspecification averse, and exhibits rare event...

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When a worker is offered performance related pay, the incentive effect is not only determined by the shape of the incentive contract, but also by the probability of contract enforcement. We show that weaker enforcement may reduce the worker's effort, but lead to higher-powered incentive...