Production takes time, and labor supply and profit maximization decisions that relate to current production are typically made before all shocks affecting that production have been realized. In this paper we re-examine the problem of stochastic optimal growth with aggregate risk where the timing...

We discuss stability of discrete-time Markov chains satisfying monotonicity and an order-theoretic mixing condition that can be seen as an alternative to irreducibility. A chain satisfying these conditions has at most one stationary distribution. Moreover, if there is a stationary distribution,...

This paper generalizes the sufficient conditions for stability of monotone economies and time series models due to Hopenhayn and Prescott (Econometrica, 60, p. 1387–1406, 1992). We introduce a new order-theoretic mixing condition and characterize stability for monotone economies satisfying...

In equilibrium models of firm dynamics, the stationary equilibrium distribution of firms summarizes the predictions of the model for a given set of primitives. Focusing on Hopenhayn's seminal model of firm dynamics with entry and exit (Econometrica, 60:5, 1992, p.~1127--1150), we provide an...

In applied density estimation problems, one often has data not only on the target variable, but also on a collection of covariates. In this paper, we study a density estimator that incorporates this additional information by combining parametric estimation and conditional Monte Carlo. We prove...

The look-ahead estimator is used to compute densities associated with Markov processes via simulation. We study a framework that extends the look-ahead estimator to a much broader range of applications. We provide a general asymptotic theory for the estimator, where both L1 consistency and L2...

We introduce a goodness of fit test for ergodic Markov processes. Our test compares the data against the set of stationary densities implied by the class of models specified in the null hypothesis, and rejects if no model in the class yields a stationary density that matches with the data. No...

This paper studies a value function iteration algorithm that can be applied to almost all stationary dynamic programming problems. Using nonexpansive function approximation and Monte Carlo integration, we develop a randomized fitted Bellman operator and a corresponding algorithm that is globally...