Business environments change over time. They are cyclic, show seasonality or just evolve over time. This is certainly true for customer demand. As a result, stationary demand distributions are crude approximations of true customer behavior at best. Yet, most classical stochastic inventory models...

This paper studies stochastic inventory problems with unbounded Markovian demands, ordering costs that are lower semicontinuous, and inventory/backlog (or surplus) costs that are lower semicontinuous with polynomial growth. Finite-horizon problems, stationary and nonstationary discounted-cost...

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...

We propose the relaxation algorithm as a simple and powerful method for simulating the transition process in growth models. This method has a number of important advantages: (1) It can easily deal with a wide range of dynamic systems including multi-dimensional systems with stable eigenvalues...

Growth models often give rise to saddle-point stable dynamic systems with multi-dimensional stable manifolds. It is argued that standard solution procedures used to numerically approximate the transition process are generally inadequate when the (stable) eigenvalues differ substantially in...