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...

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.

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The paper considers random economic systems generating nonlinear time series on the positive half-ray R+. Using Liapunov techniques, new conditions for existence, uniqueness and stability of stationary equilibria are obtained. The conditions generalize earlier results from the mathematical...

This paper studies the convergence properties of a Monte Carlo algorithm for computing distributions of state variables when the underlying model is a Markov chain with absolutely continuous transition probabilities. We show that the L1 error of the estimator always converges to zero with...