This article presents a new filter for state-space models based on Bellman's dynamic programming principle applied to the posterior mode. The proposed Bellman filter generalises the Kalman filter including its extended and iterated versions, while remaining equally inexpensive computationally....

This paper discusses solution procedures for real business cycle (RBC) models. First, we show that the most often used solution methods, the linear-quadratic approximation, the Lagrange multiplier, and the Euler equation approach all lead to the same decision function. Second, we demonstrate...

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...