In this paper we analyse the problem of the modelling of individual transitions in presence of an incomplete sampling scheme.

Two techniques for nonparametric inference concerning statistical functionals are examined as quasi-likelihood methods.

This paper proposes alternative methods for constructing estimators from accept-reject samples by incorporating the variables rejected by the algorithm.

This paper synthesises a global approach to both Bayesian and likelihood treatments of the estimation of the parameters of a hidden Markov model for the cases of normal and Poisson underlying distribution.

This paper puts forth a concept of Adptivety Rational Equilibrium (A.R.E) where agents base decisions upon predictions of future values of endogenous variables whose actual values are determined by equilibrium equations.

The simulation result of Nunes, Kuan, and Newbold suggests that it is possible to estimate a spurious break for a regression model with I(1) disturbances. In this note, we provide a rigorous proof for this phenomenon.