Measurement error causes a loss of distributional information, preventing the researcher from applying the deterministic revealed-preference tools at the individual level. This paper proposes a new statistical revealed-preference framework that is applicable to such cases. We use our framework...

Measurement error causes a loss of distributional information, preventing the researcher from applying deterministic revealed-preference tools at the individual level. This paper proposes a new statistical revealed-preference framework that is applicable to such cases. We use our framework to...

This paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and...

Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative...

Highly non-elliptical posterior distributions may occur in several econometric models, in particular, when the likelihood information is allowed to dominate and data information is weak. We explain the issue of highly non-elliptical posteriors in a model for the effect of education on income...