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...

We are concerned with a discrete-time undiscounted dynamic lot size model in which demand and the production setup cost are constant for an initial few periods and the holding cost of inventory is an arbitrary nondecreasing function assumed to be stationary (i.e., explicitly independent of time)...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

Focuses on a study which developed a framework for forecast and decision horizons. Definition of finite and infinite horizon stochastic optimization problems for a given forecast; Description of the general framework; Conditions for the existence of a solution horizon; Development of sufficient...

Recent experience with interest rates hitting the effective lower bound and agents facing binding borrowing constraints has emphasised the importance of understanding the behaviour of an economy in which some variables may be restricted at times. The extended path algorithm is a commonly used...

This paper outlines several optimization techniques useful for deriving optimal monetary and fiscal policy rules in linear dynamic perfect foresight models. Two broad categories, differing in the treatment of risk, are presented. The first group comprises certainty-equivalent optimization, based...

In this paper, we develop a rigorous new framework for the concepts of forecast and decision horizons. These concepts are conditional in nature and, in turn, enable us to unify the existing concepts of “strong” and “weak” horizons. Moreover, we are able to precisely state the question of...