Using a variation of the coupling from the past technique, this paper develops algorithms which generate independent observations from the stationary distributions of various dynamic economic models. These variates can be used for calibration, calculation of steady state phenomena, and...

We propose a generalized conditional Monte Carlo technique for computing densities in economic models. Global consistency and functional asymptotic normality are established under ergodicity assumptions on the simulated process. The asymptotic normality result allows us to characterize the...

We study a Monte Carlo algorithm for computing marginal and stationary densities of stochastic models with the Markov property, establishing global asymptotic normality and O(n^(1/2)) convergence. Asymptotic normality is used to derive error bounds in terms of the distribution of the norm...

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...

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...

This short note studies formally the common practice of log-linearizing stochastic economic models. We make precise the conditions under which stability of the original model can be inferred from that of the linearized model. A transformation to recover the stochastic equilibrium of the former...