The paper introduces a multiplicative drift condition for evaluating stochastic economic models. The drift condition is shown to permit computation of quantitative bounds for extreme event probabilities in terms of the model primitives. By way of illustration, the technique is applied to a...

This paper studies a Monte Carlo algorithm for computing distributions of state variables when the underlying model is a Markov process. It is shown that the L1 error of the estimator always converges to zero with probability one, and often at a parametric rate. A related technique for computing...

The paper studies existence, uniqueness and stability of stationary equilibrium distributions in a class of stochastic dynamic models common to economic analysis. The stability conditions provided are suitable for treating multi-sector models and nonlinear time series models with unbounded state.

This paper studies fitted value iteration for continuous state dynamic programming using nonexpansive function approximators. A number of nonexpansive approximation schemes are discussed. The main contribution is to provide error bounds for approximate optimal policies generated by the value...

This paper establishes global stability for a class of stochastic increasing returns accumulation models. The nature of the unique stochastic steady state is investigated. It is found that the models generate highly path dependent time series over long horizons. The findings demonstrate that the...

The paper gives conditions under which stationary distributions of Markov models depend continuously on the parameters. It extends a well-known parametric continuity theorem for compact state space to the unbounded setting of standard econometrics and time series analysis. Applications to...

We consider discrete time Markov chains on general state space. It is shown that a certain property referred to here as nondecomposability is equivalent to irreducibility, and that a Markov chain with invariant distribution is irreducible if and only if the invariant distribution is unique and...

The standard one-sector stochastic optimal growth model is shown to be not just ergodic but geometrically ergodic. In addition, it is proved that the time series generated by the optimal path satisfy the Law of Large Numbers and the Central Limit Theorem.

no abstract given.