This paper presents a comprehensive extension of pricing two-dimensional derivatives depending on two barrier constraints. We assume randomness on the covariance matrix as a way of generalizing. We analyse common barrier derivatives, enabling us to study parameter uncertainty and the risk...

Robust control theory is a tool for assessing decision rules when a decision maker distrusts either the specification of transition laws or the distribution of hidden state variables or both. Specification doubts inspire the decision maker to want a decision rule to work well for a ∅ of models...

Under-reaction is a robust response to model misspecification rewarded by financial markets, rather than an “irrational” attitude that leads to extinction. I show that under-reacting rules guarantee predictions as accurate as Bayes' in well-specified learning problems and beat Bayes' in many...

We introduce DOSE--Dynamically Optimized Sequential Experimentation--to elicit preference parameters. DOSE starts with a model of preferences and a prior over the parameters of that model, then dynamically chooses a customized question sequence for each participant according to an...

Most Difference-in-Difference (DD) papers rely on many years of data and focus on serially correlated outcomes. Yet almost all these papers ignore the bias in the estimated standard errors that serial correlation introduces. This is especially troubling because the independent variable of...

This paper examines the effect of systematic self-report bias, the non-random deviation between the self-reported and true values of the same measure. This bias may be constant or variable, and can mislead empirical analyses based on descriptive statistics, program evaluation and instrumental...

A new class of regression type models termed essentially linear models is proposed. The class is characterized by geometric considerations. Within the class the distribution of the maximum likelihood estimator is easily approximated by a natural extension of the p*-formula even though the MLE...