We show how it is possible to generate multivariate data which have moments arbitrary close to the desired ones. They are generated as linear combinations of variables with known theoretical moments. It is shown how to derive the weights of the linear combinations in both the univariate and the...

The full Bayesian treatment of error component models typically relies on data augmentation to produce the required inference. Never stricly necessary a direct approach is always possible though not necessarily practical. The mechanics of direct sampling are outlined and a template for including...

Bernardo and Ledoit (2000) develop a very appealing framework to compute pricing bounds based on the so-called gain-loss ratio. Their method has many advantages and very interesting properties and so far one important drawback: the complexity of the numerical computation of the pricing bounds....

Behavioral economics provides several motivations for the common observation that agents appear somewhat unwilling to deviate from recent choices: salience, inertia, the formation of habits, the use of rules of thumb, or the locking in on certain modes of behavior due to learning by doing. This...

Micro data from a dental insurance natural experiment is used to analyze why agents opt out of insurance. The purpose is to relate the dropout decision to new information on risk, acquired by the policy holder and the insurer. The results show that agents tend to leave the insurance when...